Communicating complexity - a constructionist approach to envisaging the future

A short paper by Howard Noble submitted to the Energy and people: futures, complexity and challenges conferences.

Abstract
The way people relate to energy and each other is fundamentally complex: subtle changes in agent behaviour or energy supply can radically change the system as a whole. Public discourse on energy tends to omit this complexity; we suspect over-simplification is in-part to blame for the apathy still commonplace today. We argue that agent-based computer modelling is a way to go beyond everyday language. The BehaviourComposer software builds on the NetLogo programming environment by making it possible to create models entirely within a browser. This could make it much easier for people with a wide range of skills and experiences to support each other in creating, sharing and discussing models - a dynamic that embodies the Constructionist theory of learning. Academic researchers, policy-makers and the general public could connect in this way to use computer modelling as a way to envisage future energy scenarios.

Does improved efficiently reduce energy consumption? This question has been debated since at least 1865 when William Jevons published his book The Coal Question in which he posited: “It is a confusion of ideas to suppose that the economical use of fuel is equivalent to diminished consumption. The very contrary is the truth.” The Jevons Paradox evolved into the Rebound Effect when Daniel Khazzoom argued that if a technology is made more efficient people will tend to make more use of the technology, or spend savings on other energy-intensive activities e.g. drive more miles in a car, or use petrol savings to take more flights [1].

These arguments are not particularly difficult for the layperson to understand. Most people will be accustomed to making decisions based on the price of a commodity e.g. when to make an effort to reduce consumption. If we make savings most will be used to the idea that they are then free to spend the money on something else e.g. a flight. Beyond our individual decisions we are inherently adept at imagining how other people might behave. If we decide to save energy for moral reasons (rather than simply in response to price changes) we will be acutely aware that other people might not make such noble contributions, and will perceive our own contribution in a variety of ways – social norms are powerful motivators [2]. 

If the benefits of efficiency are so contentious, and the layman inherently capable of making sophisticated decisions about how to behave within social networks - why is saving energy almost synonymous with the environmental advice published by NGOs, the press and government? Could over-simplification of the message about efficiency be a cause of some of the apathy we see today? We cannot predict with absolute certainty the effects improved efficiency will have on consumption but we can make sure we present the evidence and rationale that underpin research as clearly and openly as possible. Specifically, we could be more clear about the assumptions we make about human behaviour. Whilst it might be convenient mathematically to model people as simple self-optimising rational agents, we know intuitively that reality is much more complex. Generalisations about human behaviour tend to have an ideological slant, whether intended or not. 

The centre for connected learning and computer modelling group at Northwestern maintains the NetLogo modelling software and has created a rich library of computer models and educational materials that are used extensively by students at all levels. NetLogo embodies the Constructionist theory of learning which emphasizes the value of actively building things that demonstrate an understanding of a particular domain [3]. The group has made considerable efforts to make the NetLogo programming language easy to learn whilst still making it possible to create sophisticated models [4]. The group manages an active mailing list and have recently created an online library where people can share their models. Programming remains a difficult skill to obtain for most however and research into tools that further lower the barriers continues. 

The modelling4all.org project team at the University of Oxford has created the BehaviourComposer (BC) which builds on NetLogo by making it possible to compose computer models by assembling micro-behaviours (MBs) – pre-made snippets of useful NetLogo code embedded in web pages [5]. Once the required MBs have been assembled the model can be compiled and run in the same browser as a Java Applet. The BC comes with an introductory library of MBs that have been categorised to help people find the code they need. MBs can reside anywhere on the Internet e.g. a personal blog or a departmental website - the BC is a step towards an entirely online distributed programming environment. 

The aim in creating the BC was to turn what can be the fairly solitary experience of programming into a more social endeavour where collaboration is the norm. The design of the BC draws on the generative potential of the Internet and specifically the affordance of tools like Wikis, YouTube and Flickr where people can share and discuss their creations and learn from each other – a neat enactment of Constructionism. More specifically, the BC can be used to create a library of MBs for exploring how people relate to energy and each other. We have created a simple library of MBs to illustrate this point. The most general assumption to the model is that Agents (people) move about a landscape composed of square Patches where energy grows. The agents step through the script below:

  1. Scan neighbouring patches and record some pre-defined properties e.g. the direction to the patches with the most and least energy
  2. Decide on the preferred patch and move there
  3. Take energy from patch based on agent attributes e.g. greed
  4. Re-represent harvested energy as assets and distribute into funds e.g. for bribing other agents
  5. Interact with other agents e.g. punish a neighbouring agent if they have consumed more than the sustainable energy share of the patch.
  6. Influence neighbouring agents e.g. those with most assets change the core attributes of those with least
  7. Interact with patches e.g. agent buys a patch to restrict access
When creating the Agent script and MBs we had a particular set of research questions in mind. We wanted to explore how specific agent behaviours within a social network affect the consumption of energy e.g. how agents can influence each other to cause / prevent a tragedy of the commons. With respect to the agent behaviours we drew on research into altruistic punishment, status anxiety, bribery and notions of privacy (or obscuring information about the state of the commons). For example, to model altruistic punishment agents can see how much neighbouring agents consume and decide whether to spend some of their assets in punishing the offender. The interaction matrix is loosely based on experiments described in Altruistic punishment in humans Nature paper published in 2002 [6]. 

The aim of creating a library of MBs categorised in this way is to make it easier for other modellers to engage in the model-making process in a generative way. Not only will people be able to assemble the MBs in different combinations to create a range of models, they will also be able to tweak MBs, and eventually create their own. An online community of modellers could provide a supportive environment where the ability to program does not need to be the rate limiting step, and people with different interests and skills can support each other in the full modelling process. For example, people with field data and statistical expertise could work with programmers and other domain experts to create advanced models. (Models created with the BC can be downloaded and imported into NetLogo where the BehaviourSpace can be used to run experiments to generate model data that can be compared with field data). Interested non-experts could experiment and discuss the models made by experts and challenge the assumptions made about people. As Christakis et al have shown, influence can propagate through social networks in ways that are quite astonishing if you are not accustomed to modelling this kind of phenomena [7]. One of the most beneficial outcomes of an improved modelling literacy may be an appreciation of how easily behaviours can spread through social networks and as a consequence affect the environment. This may be cause enough for more people to reflect on how they behave in society. 

At the time of writing we have not tested the energy and people MB library described above, we plan to do this over the next few months. The project has created similar libraries and guides for academics in the Zoology and Business School departments to help students learn about the spread of a pathogen through social networks and the Sugarscape model [8]. The BC was also part of the Oxford stand at the Royal Summer Science Exhibition 2010 where people of all ages experimented with ways to stop an epidemic outbreak [9].

References:

  1. Khazzoom, Daniel J. (1980), "Economic implications for mandated efficiency in standards for household appliances.", The Energy Journal 1: 21–40 
  2. Schultz, P. W., Nolan, J. M., Cialdini, R. B., Goldstein, N. J., & Griskevicius, V. (2007). The constructive, destructive, and reconstructive power of social norms. Psychological Science, 18, 429–434. doi:10.1111/j.1467-9280.2007.01917.x 
  3. Papert, S and Harel, I. (1991) Constructionism. Ablex Publishing Corporation 
  4. Sengupta, P., & Wilensky, U. (2008, March). Designing across ages: On the low-threshold-high-ceiling nature of NetLogo-based learning environments. Paper presented at the annual meeting of the American Educational Research Association, New York, NY. 
  5. Kahn, K and Noble, H (2009) The Modelling4All Project: A web-based modelling tool embedded in Web 2.0. Paper presented at the Simutools conference, Rome, 2009: http://m.modelling4all.org/about/index.html 
  6. Ernst Fehr and Simon Gachter (10th Jan 2002). Altruistic punishment in humans. Nature 415, 137-140 | doi:10.1038/415137a 
  7. Christakis, N (2009). Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives. Little, Brown and Company. 
  8. Epstein, Joshua M.; Axtell, Robert (1996). Growing artificial societies: social science from the bottom up. Brookings Institution Press. ISBN 9780262550253. 
  9. Royal Society (2009). Emerging infections: viruses that come in from the wild. [online] Available at: http://seefurtherfestival.org/exhibition/view/emerging-infections-viruses-come-wild (Last accessed 31st August 2011).
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