Dear All,

My name is Andrea Bassi and I am one of the modelers working at this project. I will be constantly checking the discussion on this thread and will try to answer all questions. I may not be able to answer every single question or comment on every sentence, my apologies for that. Please fell free to write me an email if needed, though it would be good to keep the discussion on this forum to allow everyone to follow the discussion.

The tool chosen by ASPO-USA consist in an integrated model linking Society, Economy and Environment into one framework. The Millennium Institute has developed the T21 model over the past 24 years, in collaboration in the early phases with various professors and practitioners (e.g. MIT, Dartmouth College, Albany NY, etc.) More info at www.millennium-institute.org

T21 differs from optimization models in many ways, in fact it does not simply aim at optimizing the energy flow to minimize costs but is adds behavioral components (it looks both at the demand and supply side). The structure of the system analyzed is represented within a broad framework and causal relationship and feedback loops are the foundation of the methodology used. For this reason the model does not heavily rely on data and can capture transitions. A preliminary report on T21-USA is available at www.millennium-institute.org with an extensive explanation of the behavior of a few simulation runs.

The model is based on differential equations and it is characterized by nonlinearity, delays and a dynamic behavior created through the accumulation of flows into stocks (e.g. identified reserve -stock- oil recovery -flow- and cumulative production -stock-).
The structure of the model is built on causal relationships. Causality is the foundation of the model and correlation is one of its outputs.
Thanks to this approach, the model aims at representing reality by accounting for feedback loops (both reinforcing and balancing) observable in nature. Simulating new scenarios is also very easy and it takes about 10 seconds to simulate a 70 years period on a regular laptop (the USA model is available on MI’s website, no need to buy ay software to install it and run it). The model is transparent and we can always track changes and justify the behavior of the model.

Just to give you an idea of how the model works, population dynamics are generated by the accumulation of births, deaths, and migration into the stock of population (this stock is disaggregated into 82 age cohorts to calculate fertile women, labor force, social security and medicare beneficiaries, etc). Fertility (and births) is influenced by, among others, income, which together with emissions influences also mortality. Oil price, for instance, is influenced by the availability or oil reserves (both in the short term and in the longer term) in the US and the rest of the world (ROW), by demand/supply balance. Oil price has an impact on the economy, through total factor productivity, and on energy demand for electricity, oil and its substitutes.

T21 start simulating in 1980 and ends in 2050. The structure of the models aims at reproducing historical data until 2006 and then projecting future trends until 2050. Short-term oscillations are not the focus on this modeling exercise, we are looking at medium and longer term implications and project 5 to 10 years trends.

One of the goals of this project is to produce a policy tool able to inform policy making. For this reason a number of policy variables are included in the model and users can test their own assumptions and policies. The outcome of these changes will then be compared to the baseline scenario for the four main spheres of the model: Energy, Society, Economy and the Environment.
Since some of you mentioned games, we have been thinking of creating a game, SimCity-like, where users have to create their own future at the national and global level. The difference between the ASPO-USA model and a game, is basically the interface. SimCity and similar games are based on causality, the same foundation of the methodology used here. Users of T21-North America can simulate different assumptions and policies, we are working at a fully interactive interface (thanks to the ESF team for all the good work done so far!) and hopefully we will be able to work at a real videogame (most probably we will be working at a serious game).
Agent based is certainly an interesting methodology to do so, but it does not allow to understand where the behavior of the model comes from (please let me know if you would like to have a paper explaining the differences and similarities of system dynamics and agent based modeling). In fact, ABM builds on emergence, such an intriguing phenomena. On the other hand, our approach consist in transparency and full replicability, therefore we always want to know what facts generated a change with respect to the base scenario.

As Dick mentioned, we cannot forecast breakthrough in technology improvement of political crises. What we can do is to allow you to simulate such events in every given year of the simulation (2007 to 2050 –past events cannot be changes, but initial conditions such as total oil in place can be modified). Major improvements in oil recovery or energy efficiency (on top of what is simulated endogenously by the model) can be tested. In addition, in the T21-USA report you can find the simulation of the loss of 15% of world oil recoverable reserves (let’s say due a crisis in the Middle East). The consequences shown by the model are many: jump on oil price, higher exploration and recovery (due to the higher profitability of such investments), decline in GDP growth and energy demand, peak oil taking place earlier in time (with respect to the base case), faster transition towards renewable resources (a 5 years delay is considered to account for capacity and infrastructure building) and long term negative economic implications.

As any other model, T21 is a simplified representation of reality and it is not perfect (there is no such thing as a perfect model). This is still a work in progress and a preliminary North America version will be presented in Houston in October. Both the Millennium Institute and the ESF team are working hard to refine and improve the structure of the model and also collecting good data series.

To clarify a point on “open source”: anyone can have access to the source code of the model. In order to change it, you need to have Vensim (which has a very intuitive and user friendly interface) or be a good programmer in C. Dick was erroneously referring to the user version, which allows users to simulate various assumptions and policies without buying any software.

The model calculates carbon footprint and biocapacity. A large range of indicators can be used to “speak the language of different groups”.

John Freise and all, the World 3 model is also available for free for those who have Vensim for Ventana Systems. Let me know if you need more info on this.

Any reference or data that you think may be useful would be very helpful. We want to build an consistent model, able to identify unintended consequences and future paths of today’s choices. In order to do so we need to bring together very different schools of though and make them communicate with a single framework. We believe that T21 is such a tool, and will do our best to reach our goals.

We greatly appreciate your contribution and feedback, please keep posting and please asking questions. This is very helpful and will help us come up with a good model!

Andrea Bassi,

Millennium Institute
www.millennium-institute.org

OK, I'll throw one suggestion in here. Template a model of a prototype country, the various factors, interconnections within the country, etc. Then instantiate it for each country around the globe - with decision making on a per country basis and interconnections between individual country elements overlaid on top.

That way not only can you easily built the model from bottom up in an OO way to take account of the different situations of each country (reserves, fuel usage, sunlight, philosophy) - you can put a human in charge of each countries options and play an energy 'wargame' relatively easily.

You just to the point!
This is very similar to the project plan we are developing. The idea was to start with a North America model because the USA model was already being developed and because being an ASPO-USA project it would be nice to have a North America module well defined and hopefully used in the US.

The next step, Phase 2, include the creation of 5 additional regional models, aggregated and showing the big picture.

Phase 3 would consist in the creation of national models.

Both national and regional models are based on a starting framework and are then customized to represent specific country issues/characteristics.

The best outcome of the whole project would be to have students working from all over the world collecting data and updating the models year by year.

Hello Ms. Bassi,

Could you recommend any introductory textbooks in this field? (With math included please). Do you know of any detailed explanations of the World3 model? What kind of background is needed to work in this field?

BTW, for any TOD'ers who might want to learn more about computer models in general, check my website: http://www.myphysicslab.com. Contains simple physics simulations, interactive and real-time, with full math explanations and source code.

Don't try to predict the future. Get ready for it.

There are many books that can be useful to learn System Dynamics. The one I would recommend is certainly Business Dynamics (John D. Sterman).

This is considered to be THE book by most practicioners and professors. It contains an extensive explanation of the foundations of the methodology, it is well written and easy to follow.

There is no specific indication on the background. System Dynamics (SD) is a methodology, not a science, therefore it can be applied to a variety of fields. At the University of Bergen, Norway, where a full 2 years Master degree is offered, there are students coming from all over the world and with all educational backgrounds: economics, engineering, psychology, environmental science, sociology, etc.

SD aims at representing the structure of the system analyzed: this can be a supply chain, our brain, the economy, oil production, etc.

ps. It's Mr. Bassi (I am Italian)

I've checked the T-21 software available on your website (very nice work BTW):

You are predicting prices above $50 only after 2015 (it seems that your historical data stops in 2003-2004, already prices will probably average $70 for 2007):

World production will peak around 2022 at 36 Gb/year (~99 mbpd) which is assuming an URR around 2.8-3.0 Tb, the post peak decline seems also pretty steep:

Questions:
1. How did you model the different oil production sources (tar sands, shale oil, etc.)?
2. Can you produce error intervals?

Thank you for taking the time to look at the work already done on the USA model.

My answer to your questions follow:
1) Unconventional oil is not in the model yet, it will be included in the North America model, with tar sands in Canada mainly. Nevertheless, if you are aware of a good report or data series on production, reserves, and cost of unconventional oil, please forward it and I will try to add it before the presentation in October.
The graph you see is only showing conventional oil and liquid gas.

2) Every output of the model can be exported in MS Excel, therefore we can produce error interval. A large set of statistical tools are also available (e.g. R2)

A few comments on the graph for those who have not read the report:
a) Oil price is in real dollars (2000 is the base year). This is one of the reasons why the oil price seems lower in 2007 than it already is.
b) With T21 we project medium to longer term (5 to 10 years trends). As a consequence we look at the average price over a period longer than a year and we do not represent short term oscillations. Oil price already reached $70/bbl last year, but the average annual price in real terms was just a little above $50/barrel.
c)Historical data stop in 2007 (using the average of the first 6 months of the current year). Vertical lines in the graph show 5 years intervals.
d) We decided not to represent the oil price shock of the 80s. This is due to the fact that we want to represent the structure of the system analyzed and that shock was generated by exogenous decisions/events. Nevertheless shocks can be introduced to the model as we show towards the end of our report.
e) World oil production can change significantly when simulating different quantities of oil originally in place. Consequently URR changes as well. We ran a sensitivity analysis (Monte Carlo simulation) to show what would be the case of higher (USGS and Cambridge) or lower (ASPO) resource in place. More on this will be presented in the new report coming out in October.
f) the decline depends on technology (influenced by, among others, investment) and reserves available. The base run assumes that biofuels are available as needed, but with a 5-years delay for capacity building. This partially explains why production declines this quickly.

A nice feature of the model is that you can test different assumptions and policies and create your own scenario in less than 20 seconds. I encourage you to play with the model and to look at our presentation (July 16, New America Foundation). This would help you understand how the model works and how you can get the best out of it.

Thank you again for taking the time to review our work, any comment or suggestion on how to improve it is highly welcome!

Best,

Andrea

d) We decided not to represent the oil price shock of the 80s. This is due to the fact that we want to represent the structure of the system analyzed and that shock was generated by exogenous decisions/events. Nevertheless shocks can be introduced to the model as we show towards the end of our report.

I would dispute exogenous, particularly of a global model. The very relationships and behaviours you seek to model and understand are mediated by such decisions/events. I would go so far as to say if they are not in your model at a ground floor level, then your model will only demonstrate gradual, differential, processes. The important factors in the domain you are seeking to understand will be mediated by shocks and discontinuities.

As a 'for instance', a decision to implement a widescale coal-to-liquids programme will not be a differential effect, it will be driven by a shock event that changes the political and economic landscape. That shock event will in itself be driven by other events. Even though these are discrete events, the circumstances and timings will be tightly controlled so as to make it essentially non random.

Your model needs to deal with that.

A model cannot project a political transition or random events (unless the causes for such events can clearly be identified). What we can do is to be create a flexible model that can be easily modified beforehand (if users want to simulate various scenarios) or when such events happen.
Tipping points and minor shocks are still represented, but as you mentioned, are a result of other effects and not exogenous decisions.

In addition, if the shock has a short/medium term impact and the system follows long term trends, our approach results to be still valid.
When we refer to long term projections we want to show what are the long term implications of certain actions (e.g. policies that trigger long term change). It takes time for policies to be effective and we want to show general trends: which direction will the system take if we apply this policy?

Taking the example of the 80s, what are in your opinion the main causes for the price shock? Are they structural?