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Collaboration is crucial in design and management, fostering innovation, problem-solving, and decision-making. We explore the use of vision-language models (VLMs) for analyzing collaboration, focusing on detecting social behavior and group affect. By fusing multimodal cues, VLMs enable more context-aware reasoning beyond surface-level perception. We develop a pipeline, a structured prompt and an interactive visualization for integrating VLMs into the analysis workflow. Comparing VLM and human analysis results, we discuss how VLMs can advance collaboration analysis and the remaining challenges.
As concerns about climate change and biodiversity loss intensify, circular economy strategies are crucial for decoupling economic growth from resource depletion. Yet, the consumer behavioural dimension including returning, repairing, and accepting refurbished products remains underexplored, in particular in the bicycle industry. By conducting a survey of bicycle users, this study finds a strong willingness to engage in these slow-the-loop practices, driven by cost savings, convenience, and trust, but hindered by knowledge gaps and quality concerns, implying recommendations for manufacturers.
The transition to a circular economy requires products that encourage circular consumer behaviour. Despite the central role of designers in this transition, the design for circular behaviour (DfCB) approach remains under-explored. This paper presents a literature-based conceptual model explaining which factors need to be in place, and how they interrelate, in order for designers to facilitate circular behaviours through product design. By pointing out gaps in the current state, future research directions are suggested to foster the establishment of DfCB.
This paper addresses the lack of empirically grounded user typologies for understanding acceptance of autonomous buses in the Munich Metropolitan Area. We close this gap through a large-scale online survey and a clustering approach based on mobility preferences and subjective expected utility. The results identify five distinct clusters of users with varying acceptance levels, showing that successful autonomous bus adoption requires tailored communication, service design, and integration strategies.
This study examines designers’ cognitive and emotional experiences during the design thinking process and the effect of time constraints. Using the MetaCogno tool, 83 participants reported moment-to-moment experiences across Problem Analysis, Ideation, Evaluation, and Sketching. Positive experiences dominated, with time-limited designers showing higher enjoyment, focus, and engagement. Findings highlight the dynamic interplay of cognition and emotion, and suggest that time pressure can enhance focus and motivation during design.
This article examines the claim that experimental neuroscience is key to an improved understanding of actus reus. It focuses on the assumptions made by neuroscientists about the nature of actus reus and their principal conclusion that the voluntary act component thereof is essentially an endogenous process originating in the brain. The article contends that neuroscientists have misconstrued what lawyers and judges mean by actus reus such that their experimental findings on the subject are irremediably flawed.
Recent advances in data-driven behavioural science and, particularly, in Behavioural Data Science, saw the rise of applying natural language processing techniques to understanding and modelling human behaviour, algorithmic behaviour, as well as behaviour of complex human–machine systems. From associative modelling in the standard psychological experiments to understanding emotional arcs in novels and movies through the way humans, algorithms and complex systems use language, this chapter demonstrates how computational linguistics methods allow behavioural methodology to go beyond the ‘sterile’ laboratory environments into the field using data, test hypotheses at scale and have scalable practical impact. This chapter also shows how language-based modelling can shed light on cognition, decision-making, judgements and cultural evolution. Using empirical example of a large-scale Behavioural Data Science study, this chapter also demonstrates how language analysis can be used as a ‘truth serum’, helping to obtain underlying human preferences from subjective judgements usually provided in surveys.
This chapter provides an overview of how Behavioural Data Science can be used to understand human decision-making. It describes the methods and models used to study decision-making, including surveys, experiments and observational studies. The chapter also discusses the different types of decision-making models, including normative, descriptive and prescriptive models, and highlights their strengths and limitations. The chapter then explores the applications of Behavioural Data Science in understanding decision-making in various contexts, such as consumer behaviour, finance and healthcare. The chapter emphasises the importance of understanding the underlying mechanisms that drive decision-making, such as cognitive biases and social influences. This chapter provides a concise but informative overview of how Behavioural Data Science can be applied to understand human decisions, choices and judgements. It highlights the importance of studying decision-making in various contexts and provides insights into the methods and models used in this field. This overview serves as a foundation for further exploration of the methods and techniques used in this rapidly evolving field. The chapter concludes with a discussion of the future of Behavioural Data Science and its potential for further advancements in the study of human behaviour.
This chapter provides an overview of Behavioural Data Science and categorises it into three strands: Human Behaviour, Algorithmic Behaviour and Systems Behaviour. The Human Behaviour strand seeks to understand and predict human behaviour using large datasets in a wide variety of applications. The Algorithmic Behaviour strand seeks to improve the performance of algorithms by studying how algorithms behave as well as how they process data about humans and systems, predicting future patterns. The Systems Behaviour strand studies how humans and algorithms collaborate in complex systems. The chapter also highlights the challenges and limitations of Behavioural Data Science, including ethical considerations and potential biases in data interpretation. It concludes with a discussion of the future of Behavioural Data Science and its potential for further advancements.
Understanding the sources and consequences of luck has important behavioural and policy implications. Most prior research has treated luck as the residue of rationality, models or foresight. This chapter proposes a novel approach to help quantify the impact of luck and its interaction with human behaviours, particularly useful for behavioural data scientists. It illustrates the approach using three datasets with contexts that generate idiosyncratic patterns and behavioural implications, labelled swing luck, undeserved luck and network-bounded luck. The chapter concludes by discussing how this approach can be applied to other contexts and its scope conditions.
The Cambridge Handbook of Behavioural Data Science offers an essential exploration of how behavioural science and data science converge to study, predict, and explain human, algorithmic, and systemic behaviours. Bringing together scholars from psychology, economics, computer science, engineering, and philosophy, the Handbook presents interdisciplinary perspectives on emerging methods, ethical dilemmas, and real-world applications. Organised into modular parts-Human Behaviour, Algorithmic Behaviour, Systems and Culture, and Applications—it provides readers with a comprehensive, flexible map of the field. Covering topics from cognitive modelling to explainable AI, and from social network analysis to ethics of large language models, the Handbook reflects on both technical innovations and the societal impact of behavioural data, and reinforces concepts in online supplementary materials and videos. The book is an indispensable resource for researchers, students, practitioners, and policymakers who seek to engage critically and constructively with behavioural data in an increasingly digital and algorithmically mediated world.
We are in a polycrisis – the entanglement of crises across multiple, interconnected global systems such as climate, health, and finance – that interact to produce harms significantly greater than the sum of their parts. We propose that, to mitigate and adapt to this polycrisis, strong systemic risk governance is required, and that just and effective governance requires principles. Principles help us to identify common values, provide a framework for decision-making, and lead the necessary societal change towards a shared vision, taking on increasing importance in an ever more complex and fragile world.
Technical Summary
We are facing multiple crises, from risks across systems that are central to the safety and prosperity of humanity and ecosystems. Traditional planning and implementation have been based on command-and-control approaches with narrow objectives formulated within a constrained logic model. However, the polycrisis and addressing systemic risk require multiple objectives beyond narrow ones, which cannot address large-scale initiatives in complex, dynamic environments aimed at systems transformation. This requires a deep consideration of what objectives societies and organizations have and how they should meet them. The notion of utilizing a set of guiding principles is critical. Principles are becoming ever more prominent in considerations around the different ways in which societies, organizations, and individuals operate. Principles take on increasing importance in an ever more complex world where our effectiveness depends on adapting to context, guiding adaptation, and facilitating dialogue on options, trade-offs, and choices. We propose a set of 10 principles to guide the development of the field of systemic risk assessment and response within and across multiple domains. These principles – developed to meet the needs of the field of systemic risk – provide a complete set of operating guidelines to drive towards safety, equity, and security for human and ecological systems.
Social Media Summary
This article proposes 10 principles for systemic risk governance to navigate the polycrisis and ensure a safe future.
Addressing environmental problems like climate change urgently requires the acceleration of sustainability transitions. This Intelligence Briefing explains why this is starting to happen for technical innovations like renewable energy technologies and electric vehicles. Drawing on socio-technical transitions theory, it discusses five acceleration mechanisms that reduce cost, improve performance, change actor orientations, mobilise finance, and increase socio-political support. While not denying their potential relevance, the Briefing also shows that these acceleration mechanisms are not (yet) being activated for social innovations and deep lifestyle change. The Briefing, therefore, also criticises wishful thinking tendencies in some sustainability transformation research strands.
Technical summary
Sustainability transitions should accelerate to address environmental problems like climate change and biodiversity loss. This Intelligence Briefing aims to explain the empirical phenomenon that rapid transitions are starting to happen with regard to several low-carbon technologies (like solar-PV, wind, and electric vehicles), but not with regard to transformative social innovations or lifestyle changes. It identifies and discusses five reasons that help explain this difference: increasing-returns-to-adoption mechanisms; socio-technical feedbacks between technology, actors, and institutions; financial reorientation; issue linkage to wider political goals; and societal acceptance. It further suggests that technical innovations can act as a flywheel or catalyst for subsequent social innovations. And it makes critical comparisons between the socio-technical transitions literature and some approaches in the transformations literature, finding the former more theoretically developed, empirically validated, and policy relevant than the latter for the topic of acceleration.
Social media summary
Low-carbon technologies are starting to accelerate sustainability transitions, while purely social innovations linger.
Continued global environmental degradation generates risks to human health, for example, through air pollution, disease, and food insecurity. This study focuses on these three types of health impact and explores what drives these risks. The risks can arise from diverse causes including political, economic, social, technological, legal/regulatory, and environmental factors. We assembled diverse experts to work together to produce ‘system maps’ for how risks arise, identifying monitoring ‘watchpoints’ to help track risks and interventions that can help prevent them materialising. We critically appraise this pilot methodology, in order to improve our capacity to understand and act to protect human health.
Technical summary
Systemic risks arise through a process of contagion across political, economic, social, technological, legal/regulatory, and environmental systems. The highly complex nature of these risks prevents probabilistic assessment as is carried out for more conventional risks. This study critically explores a new approach based on participatory systems mapping with experts from diverse backgrounds helping to appraise these risks and identify data and monitoring ‘watchpoints’ to track their progress. We focus on three case studies: air quality, biosecurity, and food security. We assembled 36 experts selected in a stratified way to maximise cognitive diversity, plus 14 members of the interdisciplinary project team. Across 7 workshops, we identified 39 ‘risk cascades’, defined as pathways by which systemic risk can have negative impacts on human health, and we identified 681 watchpoints and interventions. We identify a broad range of interventions to reduce risk, exploring systems approaches to help prioritise these interventions; for example, understanding co-benefits in terms of reducing multiple different types of risk, as well as trade-offs. In this paper, we take a reflective approach, critically discussing constraints and refinements to our pilot methodology, in order to enhance capacity to appraise and act on systemic risks.
Social media summary
How can we act on the risks from air pollution, disease, and food insecurity? Insights from a new systemic risk assessment methodology.
Recent geopolitical events remind us of the need for a resilient, global approach to sustainability science. This Commentary argues that a diverse, bottom-up approach is essential to ensure sustainability science progresses, even amid shifting political processes that threaten international collaboration and funding. Locally driven solutions that value diverse perspectives and knowledge systems are vital for resilience. By supporting community-led action, sharing ideas across regions, and recognising that sustainability means different things in different places, we can build a more flexible, inclusive, and resilient path toward achieving the Sustainable Development Goals in an uncertain world.
Technical summary
Recent geopolitical events provide a stark reminder of the need to build a resilient, global approach to sustainability science. Centralised, top-down models of sustainability science are likely to be vulnerable to disruptions, from pandemics to wars, that threaten progress towards the Sustainable Development Goals and jeopardise decades of collaborative advancement that are needed to support future progress. We argue that a decentralised, community-empowered model provides the foundation needed for a resilient sustainability scientific effort. By prioritising local solutions, embracing diverse knowledge systems, and fostering horizontal knowledge exchange, we can create a more resilient and adaptable framework. Sustainability science initiatives need to elevate successful local initiatives, adopt transdisciplinary approaches that include underrepresented knowledge holders, build decentralised knowledge-sharing networks, and recognise that sustainability has different meanings across cultural and geographical contexts.
Social media summary
Decentralised sustainability science: local, diverse, and resilient in a fractious and unpredictable world.
The Earth is approaching irreversible tipping points. Markets, democracy, and technology alone cannot address these complex crises. Future Design (FD) tackles these challenges by activating human ability to prioritise future generations’ happiness over immediate gains. This research expands the FD framework and reviews a decade’s worth of studies, deepening our understanding of FD’s potential in creating mechanisms for long-term societal well-being and environmental sustainability.
Technical summary
The Earth is approaching irreversible tipping points across multiple domains. Despite advances in markets, democracy, and science, these systems systematically fail to prioritise future generations’ well-being – creating what we term ‘future failures’. New mechanisms are needed, such as FD. Originating in Japan in the early 2010s, FD aims to design, experiment with, and implement mechanisms that activate our futurability – the ability to prioritise the happiness of future generations over immediate gains – to tackle future failures. This paper introduces presentability and pastability alongside futurability, extending the FD framework. Placing various FD studies from the past decade within this framework, this study reviews mechanisms for activating these abilities and examines how activating one ability affects the others. These abilities are ‘leverage points’, as defined by Meadows. We explore the path to a paradigm shift by designing and using mechanisms that activate these points. This paper also highlights unknowns about FD and potential directions for its development, providing a comprehensive overview of its current state and future prospects in addressing global challenges.
Social media summary
Future Design: A new approach to global crises, prioritising future generations over immediate gains.
Cities, as complex systems, are faced with increasingly diverse and connected challenges across social, economic, environmental, and health domains. To help cities address these challenges, the Future Earth Urban Knowledge-Action Network developed a cross-disciplinary urban research agenda through expert elicitations and extensive consultation. Five research themes to guide urban sustainability research were identified including: (1) advancing urban sustainability transformations, (2) ensuring equity, (3) boosting innovation in low to lower-middle income countries, (4) managing complexity and systemic risks, and (5) navigating environmental change. Advancing this agenda will require collaboration across disciplines and geographies, transdisciplinary coproduction, and enhanced support to urban science.
Technical Abstract
Cities and urban regions are at the forefront of transformations toward global sustainability. As urbanization accelerates, there is increasing demand for cities to play multiple, complex and synthetic roles across social and environmental domains within and beyond their boundaries, for example driving economic development while mitigating and adapting to global environmental changes. To help cities in meeting this challenge, urban science, a rapidly growing field that includes inter- and transdisciplinary research, needs to expand and evolve, with clear priorities. Combining expert elicitation and community consultation, the Future Earth Urban Knowledge-Action Network developed a strategic research agenda for urban science for the next decade. The urban science research agenda describes five critical research themes for scientific advances: (1) accelerate urban sustainability transformations, (2) ensure equity and inclusivity, (3) amplify innovation from the low to lower-middle income countries, (4) negotiate complexity and systemic risks, and (5) navigate environmental change. Under each research theme, we review the state of the art, identify remaining gaps, and outline key research questions needing to be addressed to advance science toward urban transformations. Interconnections across, and enabling conditions to advance, these priority research themes are discussed.
Social media summary
Globally co-designed urban research agenda reveals pressing priorities for sustainability and resilience.
The world is facing multiple interconnected crises, from climate change and economic instability to social inequalities and geopolitical tensions. These crises do not occur in isolation; instead, they interact, reinforce each other, and create unexpected ripple effects – forming what is known as a polycrisis. Traditional ways of analysing problems often fail to grasp these interdependencies, making it difficult to find effective responses. We draw on system archetypes to describe and exemplify three polycrisis patterns. These provide a structured way to analyse how multiple crises unfold and interact, as well as insights into how to navigate such complexity.
Technical summary:
The concept of a polycrisis describes the complex interconnections between global issues, which can lead to unexpected emergent behaviours and the possible convergence of undesirable impacts. Understanding these dynamics is crucial for anticipating compounded effects and for identifying leverage points for effective intervention. We propose that system archetypes – generic structures in system dynamics that capture recurring patterns of behaviour – can serve as a useful analytical tool to study polycrises. Specifically, we reinterpret three key system archetypes in this context: Converging Constraints (based on the Limits to Growth system archetype), Deepening Divides (based on Success to the Successful system archetype), and Crisis Deferral (drawing from the Policy Resistance system archetype). These patterns illustrate how resource limitations, structural inequalities, and short-term solutions can sustain or worsen crisis dynamics. Using real-world examples, we show how polycrisis patterns can be employed to map feedback structures between interacting crises and to guide effective interventions. Our work contributes to a more structured and systemic understanding of polycrises, by providing a tool to help researchers and policymakers better anticipate, navigate, and mitigate their effects.
Social media summary:
‘Polycrisis patterns reveal how crises like climate change, economic instability, and inequality interact, amplifying their impacts’.
In this paper, we consider an optimal distributed control problem for a reaction-diffusion-based SIR epidemic model with human behavioural effects. We develop a model wherein non-pharmaceutical intervention methods are implemented, but a portion of the population does not comply with them, and this non-compliance affects the spread of the disease. Drawing from social contagion theory, our model allows for the spread of non-compliance parallel to the spread of the disease. The quantities of interest for control are the reduction in infection rate among the compliant population, the rate of spread of non-compliance and the rate at which non-compliant individuals become compliant after, e.g., receiving more or better information about the underlying disease. We prove the existence of global-in-time solutions for fixed controls and study the regularity properties of the resulting control-to-state map. The existence of optimal control is then established in an abstract framework for a fairly general class of objective functions. Necessary first–order optimality conditions are obtained via a Lagrangian-based stationarity system. We conclude with a discussion regarding minimisation of the size of infected and non-compliant populations and present simulations with various parameters values to demonstrate the behaviour of the model.
Service and digital transitions create a range of solutions by combining their features and introducing both human and automated agents as intermediaries. The paper classifies non/digital product/service and explores how these transitions change user involvement. A model is proposed to assess the user's role with human (service) and automated (digital) intermediaries. Utilizing user journey phases, the model is applied to four case studies, revealing commonalities in transition occurrences. Evidence suggest a potential adoption in design identifying the key phases per each transitions.