Bristol’s Local Authority Sub-System’s Causal Loop Diagram

What factors affect transition to smart local energy systems from the Local Authorities perspective?

The below model captures the causes and impacts between a set of factors that emerged form the case study of Bristol city. This model is to be used as an exploration tool to build a better understanding of impact propagation and co-dependencies. It is not for quantification of any kind.  

Press Remix then Play to exercise the below model by

increasing/decreasing impact of various factors through arrows in cycled factors 

Note: this is only a partial model; it is based only on the information from a qualitative study of Bristol’s local authority. Thus, it is both:

  • limited to inputs provided by the qualitative study participants;
  • limited to the circumstances of Bristol itself.
  • It is underspecified in terms of the magnitudes of change, as well as in terms of elements and links between them. For example, when multiple loops execute simultaneously, only the length of the link between nodes shows which loop will eventually dominate, which, clearly is not any reflection of reality.

Interactive Socio-Technical Systems (or what’s next for Ubicomp)

What is Ubiquitous Computing? If we look at samples of Ubicomp programmes taught at the universities across the web today, we find courses that include elements of HCI, Hardware and Software Prototyping, Programming,  Networking, and Creativity modules. How would then a Future-Focused New Ubicomp Programme distinguish itself from current as well as other similar programs (say Mobile Computing or HCI)? What are the defining characteristics of the Ubicomp of the future? To this end, let’s try and elicit these characteristics “by extension” from a set of newly emerging early examples of ubiquitous systems.

Some such examples are:

  • Home automation systems which employ AI to learn the inhabitants’ preferences and behaviours and to adapt the home environment (e.g., temperature, lighting, ventilation, humidity) for the comfort of those who live there (e.g., Amazon Alexa [1]);
  • Face-recognition systems in airports for seamless authorisation of legitimate passengers for flight boarding;
  • Driverless cars (e. g., Wymo [2] and Uber [3]);
  • Home energy management systems that optimise local renewables-based energy use by storing the energy generated through roof-top PV panels in the local battery for use or sale at the times when the energy price is highest, and trading the excess energy or purchasing it from market to cover own generation shortage (such as Nest [4], Tado [5], Sonnen Batteries [6]).

The apparent common characteristics of these systems are that they:

  • Are integrated into the fabric of user’s environment. For instance, the passenger in the driverless car does not need to be aware about the GPS system that enables the car’s navigation through satellite positioning, or the car’s acceleration, transmission or control systems. Similarly, the householder does not need to switch the battery or the PV panels on or off, or to go to the energy market to purchase and sell energy for use, etc.).
  • Enable the user to meet his/her needs seamlessly, without context switching or breaking their task into manageable sub-tasks to complete. For instance, the management of energy is carried out via price prediction (using machine learning); energy consumption, generation, storage as well as monitoring and control of the generating and consuming appliances (such as water heater or food freezer) are carried out through optimisation algorithms, sensors and actuators. Yet, the household has no need for understanding how to install these sensors/actuators or write and use the algorithms to get the energy management done at her house.
  • Be embodied in hardware and software automation solutions (e.g., smart meters, PV panels, battery, control software, trading algorithms, and electronic energy trading platform for energy management).
  • Represented in the society through businesses operating new business models (e.g., Sonnen [6] provides free energy to its customers if they allow the company to use a percentage of their battery storage capacity for grid flexibility services; Octopus Energy [7] operates new types of services (such as water heating) for a daily subscription charge rather than monthly utilities bill).
  • Can be operated in the intended users’ environments only upon significant change of the prevailing regulatory and legal landscapes. For example, there must be a legal recognition of the driverless vehicle and regulations must be in place for responsibility assignment in cases of accidents involving driverless cars. For renewable energy trading between households, regulations allowing individuals to buy and sell renewable energy to/from each other are necessary (currently only registered energy supplies can sell in all countries of the developed world as the electricity gird is highly regulated), etc.

Given these observations, what should then the Future-Focused New Ubicomp Curriculum constitute, and how would it differentiate itself from other related topics?

Clearly, the vision of the ‘disappearing computing’ [8] set out at the onset of the Ubicomp era still holds. The computation is disappearing into the infrastructure of the cities (becoming part of buildings, road surfaces, communication networks, wardrobe accessories and so on) and environment at large. Yet, the complexity of this ‘disappearing’ seems to have been greatly under-appreciated. It goes well beyond a single system development. To exemplify, let’s see what successful operation of a driverless car requires (sketched out in Fig. 1 below):

Fig. 1: Driverless car ecosystem

Not only do we need to ensure that:

  1. the car systems themselves (e.g., transmission, acceleration and breaking, collusion detection and prevention, fuel monitoring and replenishing, etc.) are operational and well integrated, but the car also needs to be navigated. [Sample of subject knowledge here includes mechanical, electrical, and electronic engineering, and software engineering]
  2. the navigation requires integrated input from a set of satellite systems that triangulate the current and intended locations of the car (we shall not discuss what it takes to build such a satellite and launch it into the Earth’s orbit, but this clearly is on someone’s to do list before our car can drive) [Sample of subject knowledge: telecommunications, software engineering, if not discussing aerospace related matters];
  3. the traffic control system would instruct the car on when to start and stop at on a given route [Sample of subject knowledge: control and coordination systems];
  4. a route selection and monitoring system would observe the current state of the possible route and plot a feasible and quick route for the car, ensuring that it stays on land and keeps to the operational roads [Sample of subject knowledge: maths and multi-objective optimisation algorithms, software engineering] … and this is only to be able to drive.

Now, to also ‘disappear into the background’ of the everyday, it would additionally

  1. be liked well enough to be accepted by the intended users (i.e., well designed) [Sample of subject knowledge: ergonomics, product design and HCI, experimental and social psychology];
  2. to provide a feasible operational mode for a company/institution / individual to maintain it at some cost (i.e., a business model that pays, e.g., a fleet of cars for taxi business to Uber [3], or a personal service to individuals) [Sample of subject knowledge: business management, entrepreneurship, economics, as well as psychology, and sociology];
  3. be legally/ethically/culturally acceptable within the intended user community [Sample of broad subject knowledge: law and regulation, cultural anthropology, sociology] .

And this is only what I can think of right away, without any detailed case study review.

Can a single curriculum deliver teaching and training in all these skills? Clearly not! Neither do we try to do this today in practice when developing and deploying such Ubicomp solutions as driverless car. Instead, teams of specialists collaborate to draw the required skills and knowledge into the driverless car development and deployment project.

Then maybe we could agree that Future-Focused Ubicomp Curriculum does not limit itself to Hardware and Software Prototyping, Programming, Networking, and Creativity modules, but is taught as an programme where specialists in Software Engineering, Networking, Electrical Engineering, HCI, Human Anthropology, Psychology, Business, Law, and other disciplines work in teams to build large, complex, situated, usable systems that become integrated into the infrastructure of tomorrow.  In other words, the learning to build Ubicomp (or to be more precise, Interactive Socio-Technical Systems) is not accomplished through graduating from a specific degree, but through “graduating from” working on collaborative interdisciplinary projects with focus on interaction of human (both individual and societal) and technical aspects.  In other words, I suggest that such projects should form the “heart” of the curriculum on Ubicomp/ Interactive Socio-Technical Systems, while the specific curricula would continue to deliver HCI, Communications, Software Engineering, Law, Phycology and other similar modules, depending on the flavour of the courses provided.

Acknowledgements: With thanks for the great discussions with Dagstuhl Seminar 19232: Ubiquitous Computing Education: Why, What, and How (including Nicolai Marquardt, Jeremy Cooperstock, Simon Perrault, Albrecht Schmidt, Caitlin Mills, and all the others)


[1] Alexa, URL, last accessed June 5, 2019

[2] Waymo, URL, last accessed June 5, 2019

[3] Uber driverless car, URL , , last accessed June 5, 2019

[4] Nest, URL , last accessed June 5, 2019

[5] Tado, URL, last accessed June 5, 2019

[6 ] sonnenCommunity, URL, last accessed June 5, 2019

[7] Octopus Energy: , last accessed June 5, 2019

[8] Weiser, Mark. Designing Calm Technology , 1995, URL , last accessed June 5, 2019