Joint Road Forward – a new project

It is well known that in the immediate future, cities will continue to see growth across any of the given parameters: size, demographics, pollution, economy, etc. With this future scenario and with the advent of more data collection, we wanted to look at tools and methods that would be more inclusive of people during the urban planning stage.


It is in this context that we recently started a new project to look at the issue of mobility in cities. The project is a collaboration with International Institute of Information Technology, Bangalore , TU Delft, The Netherlands and KTH Royal Institute of Technology, Sweden, focused on mobility in Indian and European cities. For this study we have selected Bangalore and the Randstad region in The Netherlands.

In this project we wanted to take a broader view of mobility and study how it is connected with the overall city in terms of livelihoods, commerce, livability and well-being. We plan to review the capability of current planning methods to incorporate the broader definition of mobility, and, to design new tools and methodologies to improve upon them.  

We are in the initial stages of our work which began a few months ago with a discussion on transport planning. We started studying the data from transport surveys. Transport is a key activity for livelihood (irrespective of location) in a city and often is an inevitable part of our expenditure when our work and home are located further away. This allows us to look at current approaches to transport planning.

Here I have to mention the that we stand on the shoulder of giants, i.e., transport planning already has a number of approaches to model travel in a city. One can look at speed (read: travel time), cost, comfort (or quality of service), etc. while designing transport infrastructure for a city. We are currently in the process of reviewing current planning methodologies.

We find that transport surveys indicated that there are groups of people who get excluded during transportation planning. Studying all the commuters allows us to create an inclusive map of travel demand across Bangalore. We will soon publish some of our methods and initial data. We have published an article on an approach to modelling people and their transport needs at APCOSEC’16 scheduled to be held in Bangalore in November 2016. A pre-publication copy and our presentation which are being prepared will be available on our website soon.

A human centric look at electricity consumption and design towards a “Smart Campus”

It was almost a year ago when we concluded a project named “The Smart Campus Simulation Tool”. We are looking to release the simulation tool to open source. In this post, we wanted to explore the problem context which informed our design.

To us a Smart Campus represented a socio-technical system that would be “malleable” enough for us to achieve our objectives. We approached it to be a socio-technical system, the technology (the adaptive sensor based control system) has to work with the social context of an academic institute. At the end of the day, people have to accept and be willing to make changes to their lifestyles.

We wanted to look at the issue of electricity consumption for the IIIT-Bangalore. The institute had invested in a fair amount of energy saving equipment such as solar panels and more efficient water heating systems. But, they were not clear about the eventual savings in energy, the electricity consumption patterns or if there was a strategy to reduce the overall consumption in the campus.

An overview of campus simulation model.
An overview of campus simulation model.

Consumption of electricity is a difficult notion to comprehend and convey. For example, when a switch is thrown, does one wonder where the electricity is generated from? It may so happen that a forest is being cleared in Chattisgarh so that you may be able to spend an extra hour on Xbox. Furthermore, we have an inherent expectation (if you grow up with some privilege,) that electricity “has” to flow if a switch is turned on. People who have no access to electricity are vulnerable in many ways to the extent that their social mobility may suffer due to lack of electricity. People who have intermittent access or pay huge bills are also cautions about consumption. Nevertheless, we seldom question the source of generation. 

Causal relationships like the one above between your consumption and environmental degradation are common and are uncomfortable (but true). Such examples try to guilt you into changing your consumption behaviour. However, it is not an easy to make lifestyle changes nor is it easy to ponder on the utility before doing everyday mundane tasks. Responsible use of electricity requires changes to behavioural and cultural practices as well as upgrades to the technical systems around us. Looking at both social and technological aspects was the cornerstone of our approach. 

We tried to look at the campus as a location which enables different people to achieve their academic goals. People in the campus perform various activities that allow them to achieve this goal. We looked at activities that consumed electricity. We then developed a simulation tool that assumed the use of sensor -based control and behavioural modification to try and check if a technology-assisted behavioural change was possible. The results of the simulation would be the base to design a serious game. The game in conjunction with sensor-based control systems would address both social and technological aspects of the issue.

Our simulation mainly consists of:

  1. a model for generating activities (explanation for what this activity means below) for various actors present on the campus,
  2. an agent based model for minimising electricity usage while keeping the comfort level of individuals at an acceptable level.

We define an activity as any action that an individual takes during the course of one’s day in the campus. A good way to model an activity is to collect detailed information using “energy dairies”. As a small academic institute, the campus had limited types of actors. We therefore chose to use a survey-based approach to collect information on daily routines. We conducted a survey to understand various daily routines for all the individuals on the campus. We also conducted interviews with some of the administrative and housekeeping staff.  We used this information to create a model for the generation of activities for various actors on the campus.

The smart campus simulation setup.
The smart campus simulation setup.

To model the “smart” systems of the campus, we created a control mechanism based on autonomous agents trying to collectively bring down the electricity consumption of the campus while keeping track of inhabitant’s comfort levels. We modelled the rooms and work areas as the autonomous agents. Each such agent was responsible for the operation of various devices that would consume electricity. It was then tasked with the objectives of minimising usage of certain devices by:

  1. negotiating the electricity consumption with other rooms (agents).
  2. Directing uses to use more common areas.
  3. Restricting when possible, the use of high power consumption devices such as air-conditioners and elevators.

In all of the above cases the it is assumed that the individual can override the agents, thus, keeping the human at the centre of the system.  (This also allows us to collect information on what sort of activities will not be compromised in the name of energy savings. ) However, a denial from the system to allow the operation of devices resulted in a decrease in the satisfaction of the inhabitants. The agents were asked to minimise the use of electricity with as little discomfort as possible for the inhabitants.

Once the models were ready we created a simulation tool and calibrated it based on the data collected by the campus for over a year on a daily basis. We could then play out scenarios such as:

  • What happens when we want to aggressively minimise consumption
  • or, what happens when the comfort for the inhabitant is paramount and
  • finally, what happens when we set a electricity consumption target for ourselves?
Calibration of the simulation, Real Data: Red, Simulated Data: Blue
Calibration of the simulation, Real Data: Red, Simulated Data: Blue


Results from using a aggressive savings scenario.
Results from using an aggressive savings scenario


Results from allowing a maximum savings scenario.
Results from allowing a maximum savings scenario
Results from using a popular choice for devices, scenario.
Results from using a popular choice for devices, scenario

It was very interesting for us to see the results and present it to the inhabitants of the campus. We are now trying to work with students to create and deploy the sensor systems at the campus. We see a potential for extending this tool to include larger spatial/network levels such as a neighbourhood or a set of neighbourhoods as opposed to a campus. We are also looking at including multiple sources of electricity, given that decentralised power and micro-grids can become popular. Furthermore, we are also exploring the possibility to include other resources such as water consumption and sewage as well into the analysis. For a more detailed description to the tool and to some other people doing similar work please refer to our paper “Krishna, Harsha, Onkar Hoysala, Krishna G. Murali, Bharath M. Palavalli, and Eswaran Subrahmanian. “Modelling technology, policy and behaviour to manage electricity consumption.” In Humanitarian Technology Conference (R10-HTC), 2014 IEEE Region 10, pp. 40-45. IEEE, 2014.”. We hope to produce and publish more results soon. In the meantime please free to check our tool at:The Smart Campus Simulation Tool

Review – Simulation and Its Discontents

Use of computer simulation in science, engineering and for design gained rapid pace in the 1980s, with the advancement of computing technology. While there is no dearth of material on the use of simulations in different fields, the impact of growing computing power on simulations and the limitations of simulations, there is little about the perceptions of simulation by its practitioners and those who shaped this particular method and technology. Sherry Turkle’s Simulation and Its Discontents is a collection of five essays on the very same topic. In her words, Turkle attempts to:


….give voice to how scientists, engineers, and designers have responded to simulation and visualization technologies as these became central to their work over the past twenty-five five years.


In the first essay, Turkle draws on two ethnographic studies she was the principal investigator for: a study of the reactions by the faculty and students to the Project Athena (an MIT project which began in 1983, and aimed at introducing the use of computing in education at MIT), and a National Science Foundation funded project in 2004 studying the use of simulation and visualization in contemporary science, engineering and design. The following four essays are case studies on the perception of simulation by its practitioners in outer space research, deep sea research, architecture, and in life-sciences pedagogy.


There is particular importance in the choice of the term “discontents”. Before reading it, it seemed to me  that the authors meant “limitations” when they said “discontents”; but the book isn’t really addressing the limitations of simulations – be it technical or otherwise. Turkle and her co-authors address how the practitioners of simulation in sciences, engineering,  design, and architecture have viewed simulation methods since their rapid diffusion into these fields since the 1980s, what their reactions to them were, and eventually how well practitioners adopted (or did not) to his new technology. The term “discontents” is probably the most apt.


Turkle presents fascinating debates around the use of simulation: how some practitioners found that it made them more creative while a few others thought it made their work more mechanical and wanted to preserve tradition in their field; how certain practitioners viewed simulation as just another tool to complete the job, and how certain others chose to incorporate it into their work and create a new ethos in design and science.


The views around use of simulation in the scientific community versus the engineering community was, to me, of particular interest. While practitioners in the engineering disciplines adopted simulation methods rather easily, we see that some practitioners in the scientific community were wary of the same. This, according to these scientists, was because engineering disciplines, unlike sciences, were not rigorous enough  and so could make do with some of the problems associated with simulation! We see that the some faculty at MIT were wary of students using simulation as-is without attaching an element of doubt around it. Given the the nature of simulation systems  (not all had the source code available; the philosophy of open source itself wasn’t mature then) and the technical training  of the faculty and students (not everyone knew how to code) then, simulations could essentially be viewed as a black box without students being able to learn the internal workings of the simulation.


However, one particular instance of the discontents stands out. This was the perspective of the chemistry faculty at MIT, to computer simulation:


…their teachers were upset by how a representation had taken on unjustified authority. Faculty began conversations by acknowledging that in any experiment, one only sees nature through an apparatus, but here, there were additional dangers: the users of this apparatus did not understand its inner workings and indeed, visualization software was designed to give the impression that it offered a direct window onto nature.


A powerful statement! This digs into the heart of what simulations are, what they are meant to do, how we as practitioners can use them, and how we ought to be well aware of its limitations and not be carried away by its “mesmerizing characteristics” (she gives instances of practitioners getting carried away as well – very engrossing but well out of scope for this article).


Turkle starts the essay by asking “What does simulation want?”; and provides an apt ending:


Perhaps we could say, with no irony, what simulation really wants – not to replace the real but to reveal.


The four case studies following the main essay focus on the Mars Exploration Rover, a deep-sea diving robot called the ROV,  architecture firms using CATIA (Computer Aided Three-dimensional Interactive Application), and in molecular biology pedagogy; and how practitioners in each of these cases dealt with simulation in their workspace. Here, the essays move away from looking only at computer simulations, to a more general meaning  of simulation: as something of the real world being represented using appropriate methods. We see how the team working the rover on Mars essentially simulated scientific expeditions using the rover, as the rover functioned as their hands, feet, and eyes on Mars. We see how architecture firms have adopted to simulation methods, which has also resulted in new forms of jobs being born. We see how simulation can also mean physical representations through even hand movements to represent molecular structures.


As a practitioner of simulations today, this was a very interesting read. However, there was one aspect constantly niggling me throughout the book; the authors have not defined what they mean by simulation, modelling and visualization and have used the terms interchangeably. That may have been on purpose, but it wasn’t clarified in the book. While these terms might have been used by practitioners interchangeably in the 1980s, it doesn’t seem like the case today, and it certainly requires more research to ascribe definitions to each of those terms. That being said, it is a thoroughly engaging read, especially for the current set of practitioners using simulations in one form or another.

Understanding the complexity of energy systems with a simulation game

This post is by Dr. Émile Chappin, Assistant Professor of Energy & Industry, Delft University of Technology, and a Visiting Researcher at Fields of View. Dr. Chappin worked with us on developing a simulation game to understand to complexity of energy systems. These are his thoughts about the complexity of the sector and how a simulation game helps in understanding it.


Vibrant Electronics City sets the scene for three weeks of intensive research on serious gaming. We are driven by the need for stability and affordability of our energy supply – they are essential for flourishing societies. That’s the reason to deal with the nitty-gritty of typical European electricity markets in which billions of Rupees or Euros are at stake but where megawatts and megawatthours are easily mixed up. The key is not only in the details: electricity markets are complex systems, of which the performance is the result of the transactions in the market, the responses to the influences from outside, such as (proposed) policies, the evolving institutions and rational or irrational expectations.


This is where we start: how can we really learn to understand the essential workings of this system? The pure nature of complexity tells us that we can’t, really. But that’s not a satisfactory answer. We should do something that helps us – students, researchers, policy makers and companies – to gain better understanding of these systems. We need to start learning how we can somehow manage the system as a whole throughout the coming decades. Not in the classical sense of management, which presumes that some form of direct control is possible. We need to find new ways of shaping the system in a (more) desired direction. How? Join us in the world of simulation games!

We would like to share four insights we learnt from complexity and developing and using simulation games and models:

  1. The notion of optimality is void. There is no perfect outcome of this system/problem. Such judgments of the system state are observer-dependent, time-dependent and cannot be predicted. One can only speak of trajectories that appear desirable or not, given a set of strong assumptions, a time-frame, a set of objectives and a delineated system.

  2. Simulation and gaming should be used as tools for discussion. Because the system we’re observing is complex, any model we make and any simulation we run is definitively wrong. That, however, does not make them useless: they can be used as a digital laboratory, our laboratory in silico. By applying many modeling and simulation techniques capturing parts of the real-world system and its problems, and using those in a variety of relevant contexts, we may get a glimpse of understanding what patterns may emerge and how we can contribute in shaping the system [1]. That is the approach for TU Delft’s Energy Modeling Laboratory [2].

  3. Experience and involvement leads to deeper understanding. The complexity in the real-world system works in counterintuitive mechanisms and leads to patterns that are hard to really understand. Our experience shows that grasping some of these patterns by experiencing them in a serious game really helps to build an intuition for the consequences of the system’s complexity [1]. That in itself implies that lessons learnt – or patterns observed – may well contribute to understanding the complexity of the real world system and any effort in shaping the system accordingly. An example in our game is the understanding that ‘simple’ economic laws such as the notion of marginal cost bidding really work (at least to a certain extent). Other examples are the irrational response to soft information of future developments, the almost unbelievable developments on world markets for fuels, the wicked trade-offs between short-term profit, market share and the reliability and affordability of energy supply in the long run.

  4. Managing is the art to use the mechanisms that drive change. Understanding and exploring what the mechanisms are that drive our societal system is extremely difficult, if not impossible. Let’s consider this management that, making use of that, an art, an “attempt to bring order out of chaos” [3]. How to know what decisions matter, what actors matter and what outcomes matter? How to measure performance? How to measure change? To answer such questions, we need to bring together theory from various fields (history, engineering, multi-actor systems, complexity, economics, policy, design, etc.) and knowledge from application domains (energy, water, transport, IT).
    We hope that simulation and gaming contributes to this process. By doing so, we make the theory operational in specific domains: we ask questions such as how we can develop and maintain an affordable electricity sector which is both decarbonized and in which supply is secured. It helps us to define what change and stability really means and how we can measure it. That way we hope to find out how we may bring about changes that put our systems on a more desired trajectory. If we can manage our precious infrastructures – the backbones of our society – that may be how.

How can a three week trip to Bangalore help gaining insight in the Dutch electricity sector? Which countries – including their energy sectors – are more different than the Netherlands and India? Well… despite the fact that the Indian and the Dutch culture are fundamentally different, both societies show many communalities. Both India and the Netherlands are quite busy: at least traffic is a pain. The fraction of the Indian population that resides in Holland may not be so far apart from the fraction of Dutch people that are in India. What Indian food is, is impossible to define, as it is for Dutch food (although for different reasons). It is easy to complain about the weather – umbrellas are a requisite in your backpack. Dutch and Indians can express themselves in peculiar ways in English. Indians like chocolate and ‘stroopwafels’ as least as well as the Dutch. And… more often than not, we can meet each other in humor.

These commonalities show that the complexity of our societies does not mean we cannot try to understand and improve them. It means we need to find new ways of doing so. The mechanisms and laws probably do not work as we expect them to! There is only one way forward: dive in the deep, experience new things, debate with an open mind, challenge all assumptions, indulge in to cultural diversity, and… embrace complexity!



[1] Chappin, E. J. L. (2011). Simulating Energy Transitions, PhD thesis, TU Delft, the Netherlands.

[2] Energy Modeling Laboratory, TU Delft.

[3] Stephen Sondheim, composor and lyricist, 2005.


Of games, gaming simulation and piracy in games

One of my fellow researchers shared the following game with me:

Game Dev Tycoon™ is a business simulation game available for Windows, Mac and Linux as well as on the Windows 8 Store. In Game Dev Tycoon you replay the history of the gaming industry by starting your own video game development company in the 80s. Create best selling games. Research new technologies and invent new game types. Become the leader of the market and gain worldwide fans.

He found it really interesting,

“What happens when pirates play a game development simulator and then go bankrupt because of piracy?”:
They ran an experiment where they released a pirated version of the game and saw how people reacted when during the simulation they ran out of money – because of piracy!

Interesting, yes. But the limitation of such games is that it is a game, as against a simulation.  An example of this effect is; most tycoon games simplify external effects as the more it is a simulation the less fun it can be. The tycoon style game play is a very popular design for  management/financial games (A close cousin is “Diner Dash” or “Farm Frenzy” which sheds light on logistics and functional parts of an organisation). Consider this, if you have played The Sims, you can find a job by simply using a computer in less than 20 seconds. But one can make an entire game out of the context of finding a job (in effect simulating the entire experience). Would you still be interested in playing The Sims if it simulates all the frustrations you experienced while finding a job?

Game Dev Tycoon simplifies the issues surrounding piracy and how it can be tackled to a great extent. Often the high price for ‘good’ games makes it inaccessible to a large audience. Some reasons for a high price may be:

  1. The game was developed using unrealistic targets. (Example: Duke Nukem Forever, no pun intended 🙂 )
  2. Games developed in mainstream  studios  Vs Indie studios

Some innovators in the field tackle piracy in the following ways  (instead of  slapping on a restrictive DRM):

  1. FTP (Free To Play) models offer a glimpse of the game before asking us to pay. The payment is usually a continuous nominal subscription versus a one time payment. The jury is still out on this. Example: LOTRO, Dungeons and Dragons, host or other games. Premium content for the players (enhancing their game-play and/or in game status) are also offered by such games.
  2. Innovative DRM systems in marketplaces  such as Steam and Uplay instead of an always on system (Example: The latest SimCity).
  3.   A low/high priced game followed by high/low paid DLCs (Downloadable Content). Example: Skyrim,  The Sims (Some DLCs for The Sims 3 is more expensive than the base game!).

Currently in Steam,  the beta version of the game is available at a lower price. The players test the game as they play beta versions and then get access to the full version for free when it is released. A very interesting experiment in reducing costs in the game development process and using the “crowd” for both testing and funding.