Case Study [a]

1. The rise of the New Urban Science

The 'New Urban Science' provides new ways of knowing and managing cities more effectively through urban data analytics and modelling. In this step, we will develop a conceptual framework to define who is involved in the New Urban Science and how they collaborate to produce new knowledge about urban dynamics. We will also examine how digital tools are being used to extend and enhance environmental, social, and economic agendas in cities.

2. Laboratories of New Urban Science

Beyond the conceptualization of the New Urban Science, it is important to study where it is being practiced. One key site of application is designated laboratories where digital tools are developed to model and assess controlled conditions. In this step, we will conduct a detailed case study of the KTH Live-In Lab, a dedicated laboratory to develop and test new digital tools. We will engage with the laboratory practitioners to understand how they design their experiments to address real world problems and how they scale up the results to realize broader impacts across the city. We are specifically interested in how the findings from the Live-in Lab can inform human decision-making processes both individually and collectively.

3. Practicing New Urban Science in the city

Beyond designated urban laboratories, the New Urban Science is also practiced in specific districts and neighborhoods to generate real-world empirical evidence. Unlike the controlled environment of the laboratory, districts and neighborhoods introduce additional complexities that influence individual and collective decision making. In this step, we will engage with stakeholders in the world-renowned district of Hammarby Sjöstad to study how they are developing and applying digital tools to enhance and extend the long-standing sustainable-smart agenda related to livability and resource consumption.

From a technical perspective, urban systems are (socially created and operated) artificial environments that draw upon planets resources (all types of material and energy) for support by increasingly complex and interconnected technology. "Intelligence" of sustainable urban systems that are resilient, robust, and adaptive to change, arises on the system-level by effectively integrating three types of very different but highly inter-dependent flows: Physical flows (material and energy), information (cyber) flows, and human/ social flows (including monetary). Thus, sustainable future city-regions can be understood as inter-connected and inter-dependent systems of the three components (P-physical, C-cyber, and H-human/social), denoted by CPHS. In the HiSS project we take steps toward a realistic CPHS integration by building on theories for the three components individually, as well as in different combinations. In particular, physical-cyber (CP, or digital analogue so called hybrid) systems have been studied over the past few decades primarily in relation to control of industrial processes. 

Humans are simultaneously autonomous and interdependent agents, acting on various levels, from individual to institutional. Since any human-social action (individual or institutional) is to some degree affected by complex histories as well as social links, actions of agents are dynamic and at best can be described probabilistically as aggregated (social) behavior. The challenge is to define a quantitative probabilistic framework for predicting the dynamics of human (agent) behavior, in form of decisions to take specified actions or conduct a given behavior. Potential applications for studying the interactions of humans with a variety of autonomous (non-human) systems in the smart cities context range from smart buildings (see also Case Study [c] and Case Study [f]) to autonomous vehicles.

The main focus of Case Study [a] is predictability of human choices/actions set in a CPHS context of sustainable urban development; these relate to individual lifestyle choices where social interactions are an important factor as addressed in Case Study [b]. 

We are developing suitable methods for quantifying human/social (H) system interactions, from individual to institutional levels that are typical for smart cities; the main challenge is to define variables and methods for quantifying the dynamics of H interactions with a predictive capability that are compatible with the hybrid (CP, Cyber-Physical) system formalisms. In any life experience, external information conveyed by the senses is interpreted and converted into percepts prior to a decision. The decision brings on a choice or an action, which has consequences on the environment and on the self, and information regarding the consequences flows back to the prefrontal cortex for cognition, see also Case Study [d]. The feedback of the decision outcomes is added to the past history, resulting in an update of the individual’s predictions, and learning. A key challenge to the individual is the complex and dynamic nature of the environment.

To be able to understand and predict the individual’s choices and actions, we examine decision-making and learning on the micro (neurobiological) scale, the meso (psychological) scale, and macro (economic-social) scale. The notion of "scale" is here to be understood as area or level of investigation traditionally the focus of neurosciences, psychology and economics, respectively. Our particular decision-making context here is related to sustainability or sustainable smart cities, which in some way bridges these different scales. The common denominator on all scales is human valuation among alternative choices, of what an individual may gain or lose by a certain choice or action. Specifically, neuro-cognitive experimental studies have shown that humans normalize or contextualize their perception of values of choice alternatives, see also Case Study [d] and Case Study [e]. On the individual (meso-scale), theories such as Theory of Planned Behavior have shown how attitudes are linked to subjective control and social norms toward forming intent for a choice or behavior. Finally, (behavioral) economic theories have addressed prospect choices with biases in subjective valuation (utilities) and risk taking. 

Our ambition in HiSS is to link these different scales in order to improve predictability of human choices with focus on lifestyle alternatives, suitably defined as prospects. Particular steps from individual to collective decision-making through social interactions, connects Case Study [a] and Case Study [b]. Also, simple strategy games will be considered as a canonical social interaction between two individuals as steps toward addressing and linking social dilemmas and social networks.

Wherever possible, we take advantage of experimental data on human choices available in the literature. These are complemented with our own experimentation either in the Live-In Lab (Case Study [b] and Case Study [c]) or through particularly designed surveys.