ACEGES Software: Smooth Centile Curves for United States

Below is the centile curves of the simulated oil production data for the United States. The distribution used for the centile curves is the Box-Cox t where all the four parameters (mu-location, sigma-scale, nu-skewness, tau-kurtosis) of the distribution were modelled (in GAMLSS) using a non-parametric Penalised Beta Spline.


Currently, we are fitting additional distributions to see if we can capture better the skewness and kurtosis of the simulated data as the above centiles are wobbly.

ACEGES Software: World Oil Production

The figures below show the simulated world oil production where simulation step 0 means year 2002. Note that the world oil production has not been modelled explicitly. The world oil production is the emergent pattern of the production behavior of the individual countries (agents). Even though these figures shows a few simulations (each line represents a single simulation), there is an interesting clustering of peak year and peak oil production.

ACEGES Software: Results for IRAN

Below are 4 figures of simulated data for Iran (black line is the actual production). The first (log representation) and third figures are based on Monte Carlo simulations of EUR, Peak Point and Demand while the second (log representation) and fourth figures are based on Monte Carlo simulations of EUR and Demand.

NOTE: These results are based on less than 100 simulations. Therefore, at this stage these figures should be treated as cartoons!

ACEGES software: Smoothing the Monte Carlo Oil Production data

Following from my last posting, these are some figures (Ecuador & US out of the 93 countries modeled) of the simulated data, generated using the ACEGES software and analysed using the R statistics software.

Black line is the actual production, blue dots is a sample of the 3.2 million (simulated) records (see last figure - US) and the density is based on the whole 'population' of the 3.2 million records.

ACEGES software: Preliminary Results

I have recently completed the ACEGES 1.0 software. This is an agent-based model of conventional oil production for 93 countries.

Due to the uncertainties with the EUR data and the oil demand, I implemented a Monte Carlo simulation that uses distributional assumptions of EUR data and oil demand based upon the data released from the Energy Information Administration, International Energy Agency, CIA and the US Geological Survey.

Next month, I will finish the User Guide and I will post the software on the CIBS website: http://www.londonmet.ac.uk/lmbs/research/cibs/cibs-scenario-planning/cibs-scenario-planning_home.cfm . If you want to access it earlier, please email me at v.voudouris@lodonmet.ac.uk

THe figure below sow the results from the US & Kuwait . RED line is the actual production while the black dots are the simulated data (77 simulation runs of 99 steps).

The ACEGES project: An ACE Model for the Availability of Global Conventional Oil Supply

I will present a paper at the 16th International Conference on computing in economics and finance. The abstract is given below:

The overall aim of this paper is to present a developing agent-based computational laboratory, termed the ACEGES (Agent-based Computational Economics of the Global Energy System) laboratory, for the systematic experimental study of the global energy system through the mechanism of Energy Scenarios. In particular, our intention is to show how Agent-based Computational Economics (ACE) and the Generalized Additive Models for Location Scale and Shape (GAMLSS) can be fused to help us understand better the challenging outlook for conventional oil supply by means of controlled computational experiments.

Specifically, the ACEGES laboratory, which is developed using the MASON (Multi-Agent Simulator Of Networks) library and the R software, models oil production curves to analyse the energy supply tensions at the national level. These production curves are semi-parametric regression models of i) original extractable oil, ii) remaining oil prior to the previous year’s production, iii) previous year cumulative production iv) previous year consumption and v) the net world demand left after an estimated demand increase, which can only be satisfied by Pre-peak Net Producer (PPNP) agent.


These semiparametric regression models are developed using the GAMLSS framework, which allows us to express macrovariables as statistical distributions. GAMLSS is a general framework of regression type of modelling in which the response variable can have a very general (up to four parameters) distribution and all of the parameters of the distributions can be modelled as linear or smooth functions of the explanatory variables. Thus, the R-based GAMLSS tool is used to build oil production curves, which are used as the decision rules of the agents. This also reflects that the R-based GAMLSS tool is integrated with the ACEGES laboratory as a way of providing a methodological advancement to undertake rigorous study of economic systems.

The Secret Life of Chaos at BBC iPlayer

Chaos theory has a bad name, conjuring up images of unpredictable weather, economic crashes and science gone wrong. But there is a fascinating and hidden side to Chaos, one that scientists are only now beginning to understand.

It turns out that chaos theory answers a question that mankind has asked for millennia - how did we get here?

In this documentary, Professor Jim Al-Khalili sets out to uncover one of the great mysteries of science - how does a universe that starts off as dust end up with intelligent life? How does order emerge from disorder?

It's a mindbending, counterintuitive and for many people a deeply troubling idea. But Professor Al-Khalili reveals the science behind much of beauty and structure in the natural world and discovers that far from it being magic or an act of God, it is in fact an intrinsic part of the laws of physics. Amazingly, it turns out that the mathematics of chaos can explain how and why the universe creates exquisite order and pattern.

And the best thing is that one doesn't need to be a scientist to understand it. The natural world is full of awe-inspiring examples of the way nature transforms simplicity into complexity. From trees to clouds to humans - after watching this film you'll never be able to look at the world in the same way again.

UNECE: Energy Security and the Financial Crisis

The UNECE Energy Weekfeatures the 18th Session of the Committee on Sustainable Energy (18-20 November), including anEnergy Security Dialogue: Impact of the Financial Crisis on Energy Industries. The annual session of the Committee will focus on the impact of the financial crisis on the volume and timing of energy sector investments and implications for energy security with the participation of executives and planners from major energy companies. There will also be presentations of views on public responses by government officials, including in some cases green economic stimulus plans among other measures to support energy industries.

The focus on energy security will continue during the working session, beginning with a survey of the major international organizations with projects and programmes on sustainable energy and energy security. The Committee will also review the work programme of its subsidiary expert groups and projects.

The programme of the 18th Session of the Committee is available at:http://www.unece.org/energy/se/pdfs/comm18/18th_EnComm_Prog.pdf

The Committee Session will be preceded by the Forum on Clean Electricity Investment and the Financial Crisis, to be held during the fourth session of the Ad Hoc Group of Experts on Cleaner Electricity Production from Coal and other Fossil Fuels (16-17 November). The Forum will address the interaction between changing technologies, evolving policy expectations and the existing and expected regulatory framework. Participants will evaluate progress in moving towards cleaner electricity production from fossil fuels, including the impact of the financial crisis and the status of large-scale projects which require a suitable regulatory framework to attract investment.

The programme of the Forum is available at: http://www.unece.org/energy/se/docs/clep_ahge4.html

An agent-based model might provide novel insights in supporting decision-making of energy-related issues. Do you know of groups/examples of agent-based models and spatial agent-based models?

ACE Research Area: Restructured Electricity Markets

ACE Research Area: Restructured Electricity Markets

http://www.econ.iastate.edu/tesfatsi/aelect.htm

The goal of this resource site is to encourage the study of restructured electricity systems from a perspective that adequately addresses both economic and engineering concerns. In line with this goal, stress is placed on research making use of powerful new agent-based computational modeling tools. These tools permit restructured electricity systems to be modeled as commercial networks of strategically interacting traders and regulatory agencies learning to operate through time over realistically rendered transmission grids.

Vlasios

UC Berkeley "Center for the Study of Energy Markets"

The UC Berkeley "Center for the Study of Energy Markets" has posted new working papers:

"Explaining the Price of Voluntary Carbon Offsets"
Download this paper in Adobe Acrobat format:http://www.ucei.berkeley.edu/PDF/csemwp193.pdf

"Doing Well by Doing Good? Green Office Buildings"
Download this paper in Adobe Acrobat format:http://www.ucei.berkeley.edu/PDF/csemwp192.pdf

"When it comes to Demand Response, is FERC its Own Worst Enemy?"
Download this paper in Adobe Acrobat format:http://www.ucei.berkeley.edu/PDF/csemwp191.pdf

"Taxes and Trading versus Intensity Standards: Second-Best Environmental Policies with Incomplete Regulation (Leakage) or Market Power"
Download this paper in Adobe Acrobat format:http://www.ucei.berkeley.edu/PDF/csemwp190.pdf

"The Implied Cost of Carbon Dioxide under the Cash for Clunkers Program"
Download this paper in Adobe Acrobat format:http://www.ucei.berkeley.edu/PDF/csemwp189.pdf

"Building Out Alternative Fuel Retail Infrastructure: Government Fleet Spillovers in E85"
Download this paper in Adobe Acrobat format:http://www.ucei.berkeley.edu/PDF/csemwp188.pdf

"What Do Emissions Markets Deliver and to Whom? Evidence from Southern California’s NOx Trading Program"
Download this paper in Adobe Acrobat format:http://www.ucei.berkeley.edu/PDF/csemwp186.pdf

Nature: Meltdown Modelling & Agent-based Models

Leigh Tesfatsion brought to my attention the two ABM-related articles published in Nature:

• Meltdown Modeling: Could Agent-Based Computer Models Prevent Another Financial Crisis?
• The Economy Needs Agent-Based Modelling

Links to download the articles:

• Mark Buchanan, "Meltdown Modeling: Could Agent-Based Computer Models Prevent Another Financial Crisis?" (pdf,1.2K), Nature, Vol. 460, August 6, 2009, 680-682.
  http://www.econ.iastate.edu/tesfatsi/MeltDownModeling.Nature080609.Buchanan.pd

• J. Doyne Farmer and Duncan Foley, "The Economy Needs Agent-Based Modelling" (pdf,922K), Nature, Vol. 460, August 6, 2009, 685-686 http://www.econ.iastate.edu/tesfatsi/EconomyNeedsABM.NatureAug2009.FarmerFoley.pdf

Vice Chancellor's 30 PhD Scholarships: Agent-based Energy Modelling

If anyone knows anyone who might be interested in PhD studentships, they are now on our web site: http://www.londonmet.ac.uk/research/the-graduate-school/vice-chancellors-phd-scholarships.cfm

An advert willappear in the Times Higher tomorrow. Details will also appear on findaphd.com and jobs.ac.uk in the near future.

Please bring to anyone's attention who might be interested, particularly the project "Energy Scenario Planning: An agent-based model of the availability of global conventional oil supply"

Ref: Hallock, J.L., Tharakan, P., Hall, C.A.S., Jefferson, M., Wei, Wu., 2004. Forecasting the limits to the availability and diversity of global conventional oil supply. Energy 29, 1673–1696.

Scenario Planning - internally consistent, sufficiently  relevant and detailed stories of what may occur in the future - has been  used as a strategic tool to cope with, but not to disguise, the  economics of uncertainty. As a qualitative framework, scenario planning  provides the ideas, elements and building blocks, which can be  communicated to leaders to allow them to cope more effectively with  uncertainty and change.   Due to the critical importance of oil to modern economic activity, and  oil’s non-renewable nature, it is extremely important to try to estimate  possible trajectories of future oil production while accounting for  uncertainties in resource estimates and demand growth. We are inviting  applications for a studentship to develop several alternate scenarios  for conventional oil supply for the period 2002-2060 using the novel  Agent-based Computational Economics (ACE) research methodology. ACE  provides a new and fundamental standard of explanation, in which one  ‘grows’ the macrophenomenon of interest (country-level oil supply),  given certain sets of microfoundations (e.g. resource availability,  future demand)  Using country-specific microfoundations of (i) the domestic consumption of oil, (ii) the projected growth rates of oil consumption,  (iii)the volume of oil originally present before any extraction (EUR),  (iv) the annual production for 2001, (v) the cumulative production to  date, and (vi)estimates of oil remaining to date, the project will  develop bottom-up simulations of county-level ‘peak oil production’

The successful applicant will be part of the ACEGES team: http://www.londonmet.ac.uk/lmbs/research/cibs/cibs-scenario-planning/cibs-scenario-planning_home.cfm

The ACEGES (Agent-based Computational Economics of the Global Energy System) Project

We launched today the webiste (http://www.londonmet.ac.uk/lmbs/research/cibs/cibs-scenario-planning_home.cfm )for the ACEGES Project.

The overall aim of this proposal is to develop, test and disseminate an agent-based computational laboratory for the systematic experimental study of the global energy system through the mechanism of Energy Scenarios. In particular, our intention is to show how Agent-based Computational Economics (ACE) can be applied to help leaders in government, business and civil society better understand the challenging outlook for energy through controlled computational experiments.

The Centre for International Business and Sustainability (CIBS) at London Metropolitan Business School (LMBS) and the Statistics, Operational Research and Mathematics Research Centre (STORM) are embarking on a major modelling exercise to support long-term UK policy analysis such as energy security and climate change. In particular the ACEGES laboratory will address the following questions:

1. How will prices affect the ratio of technically recoverable/economically extractable oil and gas reserves?
2. At what rate over time can the oil and gas from geographically dispersed nations be supplied to the marketplace?
3. How will country-level population, welfare and technological innovation affect primary-energy demand?
4. What is the environmental impact of different energy policies

Energy Challenges for Complexity Science

Below is a list of newly funded projects in the area of complexity science and energy (at various scale of analysis). 

• Complex Adaptive Systems, Cognitive Agents and Distributed Energy (CASCADE): a Complexity Science-Based Investigation into the Smart Grid Concept http://gow.epsrc.ac.uk/ViewGrant.aspx?GrantRef=EP/G059969/1
  
• reventing wide-area blackouts through adaptive islanding of transmission networks http://gow.epsrc.ac.uk/ViewGrant.aspx?GrantRef=EP/G059101/1 and http://gow.epsrc.ac.uk/ViewGrant.aspx?GrantRef=EP/G060169/1
• SCALE (SMALL CHANGES LEAD TO LARGE EFFECTS): Changing Energy Costs in Transport and Location Policy http://gow.epsrc.ac.uk/ViewGrant.aspx?GrantRef=EP/G057737/1
  
• Future Energy Decision Making for Cities - Can Complexity Science Rise to the Challenge? http://gow.epsrc.ac.uk/ViewGrant.aspx?GrantRef=EP/G05956X/1 and http://gow.epsrc.ac.uk/ViewGrant.aspx?GrantRef=EP/G059780/1

Improvise Visualisation: Visual analysis of simulations

This experimental visualization (improvise ) is being developed using Improvise for visual analysis of data produced by RimSim Pacific Rim simulation software.

RimSim Video

This can be an interesting way to 'understand' the relationship between the parameters and solution spaces of the simulations, including agent-based simulations.

Vlasios

Energy & Spatial Agent-based models

Currently, a group (CIBS and STORM) of colleagues as LondonMet are working on developing a spatial agent-based model of the global energy system using the R statistical package and Repast Symphony (the next 6 months or so I will post more information about it).

However, I have found some sources that can be helpful to people that develop agent-based model of the energy or exlectricity system:

1) Agent-Based Models of Energy Investment Decisions by Tobias Wittmann. Abstract:
At the start of the 21 st century societies face the challenge of securing an
efficient and environmentally sound supply of energy for present and future generations. Sector deregulation, the emergence of novel distributed technologies, firms focusing on these new options and competing in selected markets, and the requirements to reduce energy related greenhouse gas emissions might change the structure of energy systems significantly.
Densely populated urban areas, which allow for the operation of sophisticated energy infrastructures are the most suitable to see essential changes in their energy infrastructure.

This book develops a new model to study the development of urban energy systems. It combines a technical, highly resolved energy system model with an agent-based approach. The technical, highly resolved energy model is used to simulate the operation of technologies. Different agents are developed to capture the investment decisions of actors. Two classes of actors are distinguished: private and commercial actors. The decisions of private actors are modeled using a bounded rational decision model which can be parameterized by socio-demographic surveys. The decisions of commercial actors are approached with a rational choice model, but taking into account different perspectives of firms with regard to future
market developments.

A proof of concept implementation demonstrates the potential of the developed approach. Diffusion curves for conversion technologies and efficiency upgrades in the residential sector were obtained and the overall energy savings were calculated. Further, the impact of firms’ competition on diffusion curves could be estimated and different business models were tested.

2) Achieving A Sustainable Global Energy System: Identifying Possibilities Using Long-Term Energy Scenarios by Asami Miketa (Author), Keywan Riahi (Author), Richard Alexander Roehrl (Author), Leo Schrattenholzer (Editor). Abstract:
Sustainable development and global climate change have figured prominently in scientific analysis and international policymaking since the early 1990s. This book formulates technology strategies that will lead to environmentally sustainable energy systems, based on an analysis of global climate change issues using the concept of sustainable development. The authors focus on environmentally compatible, long-term technology developments within the global energy system, while also considering aspects of economic and social sustainability. The authors analyze a large number of alternative scenarios and illustrate the differences between those that meet the criteria for sustainable development and those that do not. As a result of their analysis, they identify a variety of promising socio-economic and environmental development paths that are consistent with sustainable development. One sustainable-development scenario and its policy implications are then presented in detail from a technology change perspective. The authors propose ambitious targets for technology adoption that are judged to achieve the desired socio-economic and environmental goals. Although the optimal policy mix to pursue these targets is clearly country-specific, the authors suggest that energy-related R&D that leads to technology performance improvements and the promotion of technology adoption in niche markets are the policy options which will yield the most significant long-term benefits. Policymakers, economists and researchers working on sustainability, energy economics, and technology change and innovation will welcome this topical and highly readable book.


3) The AMES Wholesale Power Market Test Bed by Hongyan Li, Junjie Sun, and Leigh Tesfatsion. Details: http://www.econ.iastate.edu/tesfatsi/AMESMarketHome.htm.

4) Electricity Market Complex Adaptive System (EMCAS) by Argonne. Details: http://www.dis.anl.gov/projects/emcas.html.

Vlasios

Economics and Computation (by Kenneth L. Judd)

See the email by Kenneth L. Judd. It really worths a reading!
"
Dear Colleagues,

As you all know, the so-called leaders of the academic economics have little respect for efforts to bring modern numerical and computational methods to economics. I have created a website that discusses and documents my experiences, particularly with journals. I have no illusions about the likelihood of this changing their behavior, but it does clearly show their attitude. It may also help you deal with colleagues who similarly oppose building computational expertise in economics and inflate the value of publications in particular journals.

The website is at

http://sites.google.com/site/economicsandcomputation/

I know that some will not be comfortable with this confrontational approach. In my opinion, this is appropriate given the insulting and hostile treatment that computational economists frequently experience.

Ken
"

Econophysics: Economics needs a scientific revolution

The following interesting essay is from Econophysics forum. This is written by Jean-Philippe Bouchaud who is the head of research of Capital Fund Management and a physics professor at cole Polytechnique in France. See, however, the essay by Jesper Stage on " Speaking up economic-sciences modelling" - Nature 456, 570.

Compared to physics, it seems fair to say that the quantitative success of the economic sciences is disappointing. Rockets fly to the moon, energy is extracted from minute changes of atomic mass without major havoc, global positioning satellites help millions of people to find their way home. What is the flagship achievement of economics, apart from its recurrent inability to predict and avert crises, including the current worldwide credit crunch?

Why is this so? Of course, modelling the madness of people is more difficult than the motion of planets, as Newton once said. But the goal here is to describe the behaviour of large populations, for which statistical regularities should emerge, just as the law of ideal gases emerge from the incredibly chaotic motion of individual molecules. To me, the crucial difference between physical sciences and economics or financial mathematics is rather the relative role of concepts, equations and empirical data. Classical economics is built on very strong assumptions that quickly become axioms: the rationality of economic agents, the invisible hand and market efficiency, etc. An economist once told me, to my bewilderment: These concepts are so strong that they supersede any empirical observation. As Robert Nelson argued in his book, Economics as Religion, the marketplace has been deified.

Physicists, on the other hand, have learned to be suspicious of axioms and models. If empirical observation is incompatible with the model, the model must be trashed or amended, even if it is conceptually beautiful or mathematically convenient. So many accepted ideas have been proven wrong in the history of physics that physicists have grown to be critical and queasy about their own models. Unfortunately, such healthy scientific revolutions have not yet taken hold in economics, where ideas have solidified into dogmas, that obsess academics as well as decision-makers high up in government agencies and financial institutions. These dogmas are perpetuated through the education system: teaching reality, with all its subtleties and exceptions, is much harder than teaching a beautiful, consistent formula. Students do not question theorems they can use without thinking. Though scores of physicists have been recruited by financial institutions over the last few decades, these physicists seem to have forgotten the methodology of natural sciences as they absorbed and regurgitated the existing economic lore, with no time or liberty to question its foundations.

The supposed omniscience and perfect efficacy of a free market stems from economic work in the 50s and 60s, which with hindsight looks more like propaganda against communism than a plausible scientific description. In reality, markets are not efficient, humans tend to be over-focused in the short-term and blind in the long-term, and errors get amplified through social pressure and herding, ultimately leading to collective irrationality, panic and crashes. Free markets are wild markets. It is foolish to believe that the market can impose its own self-discipline, as was promoted by the US Securities and Exchange Commission in 2004 when it allowed banks to pile up new debt.

Reliance on models based on incorrect axioms has clear and large effects. The Black-Scholes model was invented in 1973 to price options assuming that price changes have a Gaussian distribution, i.e. the probability extreme events is deemed negligible. Twenty years ago, unwarranted use of the model to hedge the downfall risk on stock markets spiraled into the October 1987 crash: -23% drop in a single day, dwarfing the recent hiccups of the markets. Ironically, it is the very use of the crash-free Black-Scholes model that destabilized the market! This time around, the problem lay in part in the development of structured financial products that packaged sub-prime risk into seemingly respectable high-yield investments. The models used to price them were fundamentally flawed: they underestimated the probability of that multiple borrowers would default on their loans simultaneously. In other words, these models again neglected the very possibility of a global crisis, even as they contributed to triggering one. The financial engineers who developed these models did not even realize that they helped the credit mongers of the financial industry to smuggle their products worldwide&emdash;they were not trained to decipher what their assumptions really meant.

Surprisingly, there is no framework in classical economics to understand "wild" markets, even though their existence is so obvious to the layman. Physics, on the other hand, has developed several models allowing one to understand how small perturbations can lead to wild effects. The theory of complexity, developed in the physics literature over the last thirty years, shows that although a system may have an optimum state (such as a state of lowest energy, for example), it is sometimes so hard to identify that the system in fact never settles there. This optimal solution is not only elusive, it is also hyper-fragile to small changes in the environment, and therefore often irrelevant to understanding what is going on. There are good reasons to believe that this complexity paradigm should apply to economic systems in general and financial markets in particular. Simple ideas of equilibrium and linearity (the assumption that small actions produce small effects) do not work. We need to break away from classical economics and develop altogether new tools, as attempted in a still patchy and disorganized way by "behavioral" economists and "econophysicists". But their fringe endeavour is not taken seriously by mainstream economics.

While work is done to improve models, regulation also needs to improve. Innovations in financial products should be scrutinized, crash tested against extreme scenarios and approved by independent agencies, just as we have done with other potentially lethal industries (chemical, pharmaceutical, aerospace, nuclear energy, etc.). In view of the present mayhem spilling over from the financial industry into every day life, a parallel with these other dangerous human activities seems relevant.

Most of all, there is a crucial need to change the mindset of those working in economics and financial engineering. They need to move away from what Richard Feynman called Cargo Cult Science: a science that follows all the apparent precepts and forms of scientific investigation, while still missing something essential. An overly formal and dogmatic education in the economic sciences and financial mathematics are part of the problem. Economic curriculums need to include more natural science. The prerequisites for more stability in the long run are the development of a more pragmatic and realistic representation of what is going on in financial markets, and to focus on data, which should always supersede perfect equations and aesthetic axioms.

An edited version of this essay appeared in Nature (455, 1181, 30 October 2008)

Emergent Macroeconomics: An agent-based approach to business fluctuations

Although I have not finished the book yet, I have found it really interesting. It gives good examples of the interactions between microeconomics and macroeconomics. It achieve this by using agent-based models to establish sound microfoundations of macroeconomics.

Abstract (From the Publisher): This book contributes substantively to the current state-of-the-art of macroeconomics by providing a method for building models in which business cycles and economic growth emerge from the interactions of a large number of heterogeneous agents. Drawing from recent advances in agent-based computational modeling, the authors show how insights from dispersed fields like the microeconomics of capital market imperfections, industrial dynamics and the theory of stochastic processes can be fruitfully combined to improve our understanding of macroeconomic dynamics.

A copy can be downloaded from: http://www.dea.unian.it/gallegati/Emergent_Macroeconomics.pdf

Agent-based models (new book by Nigel Gilbert)

Nigel Gilbert contributed recently to the "Quantitative Applications in the Social Science" series by writting about 'agent-based models'. The series editor (Tim Liao) writes:" there are two general approaches to the study of social behavior. collect observational, survey, or other forms of data and analyze them, possibly by estimating a model; or begin from a theoretical understanding of certain social behavior, build a model of it and then simulate its dynamics to gain a better understanding of the complexity of a seemingly simple social system". Gilbert's recent work belongs in the second tradition which is usually termed Generative Social Science after Epstein and Axtell (1996)

I found chapter 3 (using agent-based models in social science) really useful as it gives practical steps in developing an agent-based model. Chapter 2 is also important as it discusses explicitly the concepts of time (as well as the concepts of agents and environment). Although he discusses time briefly, his treatment is important because it is usually underestimated. Within the GIScience literature, Dona Peuquet' s book (Representation of Space and Time) provides a comprehensive discussion that may benefit the development of agent-based models.

My personal view is that time has been trivialized in most of the agent-based models that I know. Time should be added to the list of significant research areas (e.g.,space/GIS, Learning and Simulation of language - chapter 5) that agent-based modellers need to address.