The Object-Field Model

Call for Contributors: Global Energy Policy and Security

2011-06-17T09:40:00.002+01:00

Prof. Walter Leal Filho (Head of Research and Transfer Centre, Applications of Life Science Hamburg University of Applied Sciences,
Germany) and Dr. Vlasios Voudouris (Deputy Director Centre for International Business and Sustainability London Metropolitan Business School) are editing a new book with the title: "Global Energy Policy and Security." The book aims to represent a multidisciplinary perspective. Therefore, contributions from energy economics and agent-based computational economists working in the field of energy markets are particularly welcome. For example, a book chapter will be based on the Agent-based Computational Economics of the Global Energy System (ACEGES -http://www.aceges.org) project as a way of demonstrating the use of the agent-based computational economics (ACE) for exploratory energy policy by means of controlled experiments.

For details, please see http://www.haw-hamburg.de/call_for_papers_en.html

ACEGES: New article at Energy Policy

2011-06-10T20:12:00.001+01:00

Abstract
An agent-based computational laboratory for exploratory energy policy by means of controlled computational experiments is proposed. It is termed the ACEGES (agent-based computational economics of the global energy system). In particular, it is shown how agent-based modelling and simulation can be applied to understand better the challenging outlook for oil production by accounting for uncertainties in resource estimates, demand growth, production growth and peak/decline point. The approach emphasises the idea that the oil system is better modelled not as black-box abode of ‘the invisible hand’ but as a complex system whose macroscopic explananda emerges from the interactions of its constituent components. Given the estimated volumes of oil originally present before any extraction, simulations show that on average the world peak of crude oil production may happen in the broad vicinity of the time region between 2008 and 2027. Using the proposed petroleum market diversity, the market diversity weakness rapidly towards the peak year.
Highlights
► Development of oil scenarios using computational experiments. ► Support of energy policy using agent-based modelling and simulation. ► Demonstration of the agent-based computational economics of the global energy system (ACEGES) policy-support tool. ► Energy policy by means of evidence-based forward-looking probabilistic forecasts.
Keywords: Oil depletion; Oil scenario generation; ACEGES

For more information, see http://www.sciencedirect.com/science/article/pii/S0301421511003867

ACEGES: New article at Energy Policy

2011-06-10T20:12:00.000+01:00

UK Energy Day: Video

2011-04-14T18:07:00.001+01:00

MSc in Sustainable Business, Energy and Finance

2011-03-29T15:24:00.008+01:00

Open publication - Free publishing - More CIBS

aceges.org

2011-03-07T19:06:00.001Z

The results of the ACEGES project, including updates of the ACEGES software, will be hosted at www.aceges.org from March, 2011 onwards.

ACEGES Presentation at UCL Energy Istitute

2011-03-01T15:17:00.000Z

UK Energy Day 2011

2011-02-07T14:44:00.004Z

The Centre for International Business and Sustainability at LondonMet Business School is organising the UK Energy Day: Sustainably Supply. The UK Energy Day 2011 is part of an European network of events led by the Intelligent Energy Agency of the European Commis- sion. The event will promote the awareness of the significance of sustainable energy supply, particularly the need to balance home-grown energy and energy imports. During the day, talks and demonstrations will take place to increase awareness of sustain- able methods of energy production at the global, regional, local and industrial levels.

The brochure and registration form of the event is available here: http://www.londonmet.ac.uk/lmbs/research/cibs/energyday.cfm

ACEGES: Launching the ACEGES demo

2011-01-18T16:27:00.010Z

We released the ACEGES demo as a Java applet. The most reliable web browser for the ACEGES demo seems to be Google Chrome (both for Mac and Windows). Please let me know if something is in need of revision or what kind of functionality you would like to see by emailing me at v.voudouris@londonmet.ac.uk

To access the ACEGES demo, select the "ACEGES Applet" tab from here: http://www.londonmet.ac.uk/lmbs/research/cibs/aceges

The ACEGES laboratory for energy policy: Exploring the production of conventional oil

2011-01-11T20:27:00.002Z

I am giving a talk at the UCL Energy Institute on January 25th. In case, you are interested to attend.

Abstract:
For exploratory energy policy by means of controlled computational experiments, I propose an agent-based computational laboratory, termed the ACEGES (Agent-based Computational Economics of the Global Energy System). In particular, I show how agent-based modelling and simulation can be applied to understand better the challenging outlook for oil production by accounting for uncertainties in resource estimates, demand growth, production growth and peak/decline point. This approach emphasises the idea that the oil system is better modelled not as black-box abode of 'the invisible hand' but as a complex system whose macroscopic explananda emerges from the interactions of its constituent components. The simulated scenarios, paints of plausible futures satisfying the generative explanation, show that the peak of world oil production is likely to happen in the region of 2020 and 2040 with a global minimum of remaining reserve/production ratio between 20 and 40. Using the proposed petroleum market diversity, I also observe a global minimum of market diversity in the broad vicinity of the peak year.

For details, please see http://www.eventbrite.com/event/1182949233

Conrad Wolfram: Math with Computers

2011-01-08T11:17:00.001Z

This is really just want we need. Worth looking at it.

Thanks Carlo for bring this to my attention.

The Joy of Stats: Hans Rosling

2010-12-07T16:02:00.002Z

this is certainly something worth watching.

and this is a sample of what will be presented today at 21:00 at BBC Four

Vlasios

New Article: Towards a unifying formalisation of geographic representation: the object-field model with uncertainty and semantics

2010-11-26T21:15:00.007Z

Abstract: The need for a conceptually unifying data model for the representation of geographic phenomena is widely understood. Although some successes have been reported, progress has been slow, especially at the conceptual and logical levels of abstraction. Drawing on and combining existing successes, this article suggests the object-field model with uncertainty and semantics at the conceptual and logical levels of abstraction. The logical level has been formalised in the Unified Modelling Language (UML) class diagram. It is shown that the concepts required to better represent geographic phenomena can be derived from a single foundation that is termed the Elementary_geoParticle with associated uncertainty and semantics by means of aggregation. The town centre phenomenon is used as an application of the conceptual framework being proposed.

To download the paper: http://www.informaworld.com/smpp/content~db=all~content=a930247080~tab=content

Video of oil production

2010-11-23T01:15:00.006Z

The data has been generated in ACEGES and animated in R.

ACEGES Software: The Interaface

2010-10-27T16:59:00.006+01:00

Below is the latest interface of the ACEGES 1.1 software, with the emphasis on the development of exploratory scenarios for oil and gas production.

Screen 1

Screen 2

Benoit Mandelbrot 1924-2010

2010-10-19T11:03:00.004+01:00

Benoit Mandelbrot, the mathematician, the father of fractal mathematics, and advocate of more sophisticated modelling in quantitative economics and finance, died on 14th October 2010 aged 85.

For a compilation of videos see: Benoit Mandelbrot

Sustainability and Business

2010-10-13T22:17:00.002+01:00

Although the video is not directly related with the aims of the blog, I really like it. That is why it is here.

Maybe, novel modelling tools can help us see things differently.

PhD Opportunities at LondonMet

2010-09-20T09:07:00.005+01:00

There are a number of PhD opportunities at LondonMet. Please see here: http://www.londonmet.ac.uk/londonmet/research/the-graduate-school/vice-chancellors-phd-scholarships.cfm

In particular, see the following proposals ( http://www.gamlss.com/papers/PHDoportunities.pdf) :

i) Data Mining Models for the Flexible Modelling of the Location, Scale and Shape Parameters of a Response Distribution
ii) Comparison, Evaluation and Development of Stochastic Volatility Models
iii) Energy-Economy-Investment Modelling
iv) Individual decision-making and social interaction at the movies: The Philadelphia Story

Fusing the agent-based and Object – Field models

2010-08-24T21:13:00.007+01:00

One of my recent works appeared as an advance online publication at the Environment and Planning B: Planning and Design. The url of the paper is: http://www.envplan.com/abstract.cgi?id=b36001

Abstract. The fusion of agent-based and geospatial models represents an exciting new synthesis for social science and economics. It has the potential to improve the theory and the practice of modelling complex real-world phenomena. Yet, to date, there has been little systematic analysis at the conceptual and logical levels of how to fuse agent-based and geospatial models for the representation and reasoning of socioeconomic phenomena. Here both sets of issues are explored. In particular, it will be argued that the development of synthetic models requires autonomous agents and flexible organisational structures that can complete their objectives while situated in a dynamic and uncertain geoenvironment represented by the concept of Elementary_geoParticle. As an example of the concept, I present a preliminary conceptual model of global energy to demonstrate the validity and possible uses of the proposed technique.

The modelling framework discussed above has been used in the ACEGES project discussed here: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1652361. A new paper is currently under review in an energy journal (i will not name the specific journal until the review process completes).

If you are interested in the topic of agent-based and geospatial models, see the gisagents blog ( http://gisagents.blogspot.com/ ) by Dr Andrew Crooks.

Simulated Scenarios of Convetional Oil Production

2010-08-24T09:29:00.007+01:00

I will present a paper at the 4th International Conference on Computational and Financial Econometrics hosted by University of London & London School of Economics. See here for details: http://www.cfe-csda.org/cfe10/ The paper may also appear at the Computational Statistics & Data Analysis journal.

Abstract:
The ACEGES (Agent-based Computational Economics of the Global Energy System) model is an agent-based model of conventional oil production. The model accounts for four key uncertainties, namely Estimated Ultimate Recovery (EUR), estimated growth in oil demand, estimated growth in oil production and assumed peak/decline point. This work provides an overview of the ACEGES model capabilities and an example of how it can be used for long-term scenarios of conventional oil production. Because the ACEGES model has been developed using the Agent-based Computational Economics (ACE) modelling paradigm, the macro-phenomenon of interest (world oil production) grows from sets of micro-foundations (country-specific decision of oil production). The simulated data is analyzed in GAMLSS (Generalised Additive Models for Location Scale and Shape). GAMLSS is a general framework of modelling where the response variable (oil production) can have a very general (up to four parameters) distribution and all of the parameters of the distributions are modelled as linear or smooth function of the explanatory variable (e.g., time). From a methodological perspective, ACEGES and GAMLSS are applied to help leaders in government, business and civil society better understand the challenging outlook for energy through controlled computational experiments.

Probabilistic forecasts of oil production

2010-08-13T15:13:00.004+01:00

Following from my last posting, see the two "improved" figures (analysis conducted with Mikis Stasinopoulos and Bob Rigby).

The H-H scenario:

The L-M scenario:

L-M and H-H Scenarios of oil forecasting

2010-08-12T12:25:00.008+01:00

I have created two scenarios of conventional oil production and below you can see the simulated results and the the historical production from 2000 to 2009 (black dots). Given the four key uncertainties (demand growth, production growth, EUR and peak/decline point):

1)The L-M case uses estimates from the demand growth, production growth, EUR and the Monte Carlo process for the peak/decline point (this is the low-medium heterogeneity)

2) All key uncertainties are drawn from the Monte Carlo process (this is the high heterogeneity case)

Note: the simulation is initialized with the 2001 data. Thus, the first simulated year is 2002.

Benoit Mandelbrot: Fractals and the art of roughness

2010-08-12T10:53:00.000+01:00

I hope you will enjoy the video as I did:

Presentation of ACEGES at the CEF 2010

2010-08-06T13:16:00.003+01:00

This is the presentation that I have at the Society for Computational Economics, 16th International Conference on Computing in Economics and Finance, London, UK.

The ACEGES 1.0 Documentation

2010-08-02T22:19:00.005+01:00

We have recently completed the first draft of The ACEGES 1.0 Documentation: Simulated Scenarios of Conventional Oil Production.

Abstract: The ACEGES model is an agent-based model of conventional oil production for 93 countries. The model accounts for four key uncertainties, namely Estimated Ultimate Recovery (EUR), estimated growth in oil demand, estimated growth in oil production and assumed peak/decline point. This documentation provides an overview of the ACEGES model capabilities and an example of how it can be used for long-term (discrete and continuous) scenarios of conventional oil production.

The documentation can be accessed from: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1652361

Citation:
Voudouris, V. and Di Maio, C. (2010), The ACEGES 1.0 Documentation: Simulated Scenarios of Conventional Oil Production, Centre for International Business and Sustainability (London Metropolitan Business School): Working Paper 12, London, UK.

VC PhD Scholarships: ACEGES project

2010-07-29T10:55:00.011+01:00

Proposal 1- Energy-Economy-Investment Modelling: Volatility of oil prices and oil supply
Applications are invited in the area of Energy-Economy-Investment Modelling. The applications should be within the scope of the ACEGES project (http://www.londonmet.ac.uk/lmbs/research/cibs/cibs-scenario-planning/cibs-scenario-planning_home.cfm).

In particular, we encourage applications that aim to investigate the relationships between:
i) Spot price of crude oil,
ii) Expectations of future oil prices
iii) Price of crude oil futures
iv) Oil futures spread (defined as the percent deviation of the oil futures price from the spot price of oil)
v) Oil supply shocks.

The overall research aim is to investigate what determines the spot and futures price of crude oil and the importance of the evolution of the price of oil in explaining oil production of OPEC and non-OPEC countries.

Applicants are expected to have a Master’s degree with a strong quantitative, mathematical or statistical focus. Programming experience in Java and/or R is desirable but not essential.

References:
1) Hamilton, J. (2003), What is an Oil Shock? Journal of Econometrics, vol. 113(2), 363-398
2) Alquist, R and Kilian, L. (2010), What Do We Learn from the Price of Crude Oil Futures? Journal of Applied Econometrics, 25(4), 539-573.
3) Kaufmann, R.K. (1991), Oil production in the lower 48 states: Reconciling curve fitting and econometric models. Resources and Energy, 13(1), 111-127

In the first instance, contact me at v.voudouris@londonmet.ac.uk

Proposal 2 - Consumer decision-making and social interaction at the movies: The Philadelphia Story
When faced with choices between new products and services, the properties of which are uncertain, how do individuals behave? Economists commonly distinguish between social and private information available to the consumer - social learning takes place through social networks, while private learning is the consequence of past experience. In factoring both into the decision-making process it is possible to model consumer behaviour - in this instance film consumer behaviour.

To do this I propose developing an agent-based model, which allows the researcher to investigate the microscopic behaviour of individual film consumers who watched particular films at particular cinemas at particular moments in time in order to a) grow the macroscopic environment manifest in the long tail distribution of revenue, b) chart the pattern of diffusion from box-office rich to box-office poor cinemas, and c) forecast the closing box-office of films that opened in the first-run cinemas from the opening Saturday night of the release.

The dataset that forms the basis of this proposal is drawn from the city of Philadelphia for the years 1935-36. Housed in the Warner Bros. Archive at the University of Southern California, I have recently uncovered the weekly billing sheets of 91 cinemas located in the city belonging to the Stanley Warner chain. The sheets provide micro data of an unparalleled nature about audience choices, consisting of daily box-office returns generated by the films screened at the 91 cinemas. This body of data has never previously been accessed. Once transcribed onto a database and analysed, it will make possible a much fuller understanding of audiences and the choices they made. Furthermore, the work will be directly applicable to contemporary consumer behaviour concerning experience goods.

I am looking for a graduate student that has an excellent quantitative historical background, who is excited about executing detailed fieldwork in the city of Philadelphia; building a dataset from archive materials using relational database software; and finally modelling the data to investigate consumer behaviour. Knowledge of a programming language is desirable.

ACEGES Software: video- World Oil Production

2010-07-12T02:27:00.015+01:00

Below is video of World Oil Production for 5 Monte Carlo experiments. The first graphic is a stacked timeseries representing the contribution of each country - only countries that produce for the specific experiment are shown. The second graphic is shows oil production and reserve/production ration. I thank Maciej M. Latek for his help in the design of the graphics.

The first simulated year is 2002.

A better quality video can be seen at:
http://screencast.com/t/OWM4YzkzYz

ACEGES Software: video

2010-07-10T01:09:00.017+01:00

Although this is not a good video (not sure how to convert swf to mpeg), it show the 'world oil production' and the production of 'Saudi Arabia' for certain Monte Carlo Experiments of the ACEGES.

The lines with the 'left colors' show the 'Production' and the lines with the 'right colors' shows the 'Remaining Reserves/Production'.

NOTE: the world oil production has not been modelled explicitly. This is the macro-regularity of the decisions made by the agents - the 93 countries modeled in ACEGES 1.0.

When the graphics show different combinations of colors, this signals a new simulation with different starting assumptions for key uncertain variables such as EUR, peak/decline point and oil demand.

The ACEGES Project: Presentation

2010-07-10T00:30:00.003+01:00

This is a general presentation that I give about the ACEGES project.

ACEGES Software: Probabilistic Forecasts of World Oil Production to 2100

2010-06-22T22:53:00.014+01:00

I have been asked by a few people to show a graphic of World Oil Production. This is given in the figures below. I have superimposed the 'peak year' based upon the simulation (Monte Carlo simulations based upon distributional assumptions of OIL EUR, Peak Point, Oil Demand).

Although the peak year and peak production contradict much of the existing views, a full explanation of the assumptions of the ACEGES program and the data used to initialize the simulations will be provided in the ACEGES 1.0 manual - expected by the end of July. The figures are based on only 134 simulations.

The blue dots is a random sample from the simulated data and the black lines (in the figure below) are estimated centiles using the Generalized Beta type 2 distribution. All the four parameters of the distribution were modelled using cubic smoothing splines. The actual production, red line, seems to follow the 2% centile value.

ACEGES Software: Smooth Centile Curves for United States

2010-06-21T22:02:00.013+01:00

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

2010-06-20T18:02:00.004+01:00

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

2010-06-20T14:08:00.009+01:00

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

2010-06-19T06:27:00.012+01:00

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

2010-06-18T22:11:00.009+01:00

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

2010-04-14T21:08:00.004+01:00

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

2010-01-19T11:48:00.002Z

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

2009-12-05T14:37:00.004Z

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

2009-10-02T12:02:00.002+01:00

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"

2009-09-09T22:28:00.002+01:00

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

2009-09-09T07:59:00.004+01:00

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

2009-09-03T18:40:00.002+01:00

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

2009-07-21T17:09:00.004+01:00

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:

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

Energy Challenges for Complexity Science

2009-06-09T16:37:00.002+01:00

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

2009-05-27T19:09:00.010+01:00

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

2009-05-21T09:47:00.007+01:00

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)

2009-04-02T16:27:00.002+01:00

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

2009-02-07T17:01:00.005Z

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

2009-02-02T20:32:00.004Z

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)

2009-01-14T16:51:00.008Z

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.

PhD thesis of the Object-Field Model

2008-10-29T19:58:00.005Z

The full PhD of the Object-Field Model can be accessed from http://vega.soi.city.ac.uk/~fd776/phd/PhD_VoudourisV.pdf OR http://ssrn.com/abstract=1292262

ABSTRACT

The need for a conceptually unifying data model for the representation of geospatial phenomena has already been acknowledged. Recognising that the importance of the data model employed by and large determines what can be done by way of analysis and the methods by which the analysis can be undertaken, there has been some activity in developing unifying data models for geospatial representation in digital form. Some successes have been reported. Nevertheless, progress has been slow, especially at the conceptual and logical levels of abstraction of geospatial data models.

Concepts and ideas from cognitive and perceptual psychology as well as GIScience and GISystems literature are examined within the context of geospatial data modelling and reasoning. Drawing on and combining these concepts, ideas and successes with an empirical approach which proposes generalities by induction, this thesis suggests the fused Object-Field model with uncertainty and semantics at the conceptual, logical and physical levels of abstraction. The logical level has been formalised in the Unified Modelling Language (UML) class diagram and the physical level has been implemented in Java programming language.

The purpose of the Object-Field model is to better support the representation and reasoning of geospatial phenomena, particularly indeterminate phenomena such as town centres and land cover changes. It is shown that many of the concepts required to better represent geospatial phenomena can be derived from a single foundation that is termed the elementary-geoParticle which is regarded as indivisible, has no parts and serves as the standard for integrating the dual continuous-field and discrete-object conceptualisations by means of aggregation. A second concept is introduced, termed Traditional Scientific and Concept spaces of the Object-Field model and shown to provide a useful foundation for collaborative reasoning. The traditional scientific space is a mathematical representation of observational data and the concept space is a representation of conceptualisations, meanings and interpretations of the traditional scientific space. A third concept is also introduced, termed the Hierarchical Uncertainty and Semantic components of the Object-Field model that ‘populates’ the concept space with variable levels of uncertainty and semantics. Sketching is also suggested as a way to represent, record and manage conceptualisation uncertainty as it is an element of uncertainty that is frequently overlooked, yet has a significant impact in the way in which subjects understand and use geospatial data. Given that conceptualisation uncertainty is a subjective process that varies between individuals, this form of uncertainty has particular importance in the collaborative decision-making of indeterminate phenomena.

This thesis constructs technical and theoretical scientific knowledge for the design and development of geospatial models that aim to support the human decision-making process of indeterminate phenomena by means of multiple conceptualisations and interpretations. The theoretical knowledge is embodied in the UML formalization of the Object-Field model and the technical knowledge is embodied in the Object-Field GISystems Prototype.

The Object-Field model with Uncertainty and Semantics

2008-10-17T15:11:00.002+01:00

Having completed the PhD (October 15, 2008), I will publish parts of the PhD chapters in the blog next month. I will also make available the Java code of the Object-Field GISystems Prototype.

Understanding Anasazi using Agent-Based Modelling

2008-10-12T12:06:00.006+01:00

'The 1050 Project' has as an output a synthetic agent-based model. It incorporates an empirical landscape with artificial Anasazi agents. Note that the landscape is a reconstruction of seven production zones based on Archaeological and other information in Long House Valley, northeaster Arizona.

More information is included in the chapter 4-6 of the Generative Social Science book by Epstein and others. A Java-based application of the Anasazi can be accessed from http://ascape.sourceforge.net/

SimCity Creator for Wii

2008-10-10T18:54:00.004+01:00

There is a game called "SimCity Creator Wii" which enables you to create your own City.

I wonder if these games will help in raising awareness of the complexity of spatial modelling (as Google Earth/Maps seems to do) or they will trivialize this complexity by oversimplifying it.

See also: http://www.freehand.co.uk/games/seera/ by SOUTH EAST ENGLAND REGIONAL ASSEMBLY

UNDERSTANDING SUSTAINABLE DEVELOPMENT AND URBAN ECONOMIC GROWTH: EXPLORATIONS WITH AN AGENT-BASED LABORATORY

2008-09-11T08:34:00.003+01:00

I currently work in the idea that there is no conflict between the concepts of sustainable development and economic growth, as some people suggest.

If we accepts that ‘Sustainable Development’ is a catchall term about intergenerational welfare, then optimal allocation of stock capital can support both sustainability and economic growth. In this case, stock capital includes both environmental capital (such as fossil fuel and clean water) and man-made capital (such as school and hospital).

If the above is accepted, then what is the difference between economic theories of maximisation of welfare and sustainable development? I try to answer these questions within a triad conceptual framework of economy, effectiveness and efficiency, and an spatial agent-based computational laboratory.

But what is 'sustainable development? WCED (1987) and UK Defra (2008) define sustainable development as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs”. Although this definition seems clear, it does not provide a conceptual basis for measuring sustainable development in a systematic way (Beckerman 2003). For example the intra-generational needs of the people coevolve in space and time without necessarily satisfying all the present needs at any point in space and time.

Pezzey (1992) argues that sustainability is related with measures that sustain an improvement in the quality of life, which is also supported by Faucheux et al (1996) who emphasise the need for intergenerational equity in the context of non-negative change in economic welfare per capita. These definitions signal a shift in defining sustainability by promoting the concept of ‘welfare’ as an all-embracing central variable, as argued above. Beckerman (2003) argues since the whole problem is the selections of means towards ‘sustainability’ and the assessment of these means, then the concept of sustainable development has nothing to add (if not subtracting from the classical economic objective of maximisation of welfare because of the precautionary principle).

I try to critically analyze these premises within the ‘triad framework’ (economy, efficiency and effectiveness). Based on this triad framework, I propose ways to measure sustainable development in the context of urban economic growth using a spatial agent-based computational laboratory. I view this laboratory as step forward in addressing what the UK DOE (1996) argues: it is not clear what sustainable development means, thus it is difficult to know how to measure it or which policies promote it.

COMBINING THE ADVANTAGES OF AGENT-BASED & AND EQUATION-BASED APPROACHES

2008-09-06T14:54:00.007+01:00

I am currently working on ways to better understand the interaction (or tension according to some) between sustainable development and economic growth as a means of informing policies about intra- and intergenerational welfare. The approach that i take is to use a fused top-down and bottom-up approach by integrating equation-based and agent-based models.

Bobashev and Epstein (2007) publish a relevant paper: A Hybrid Epidemic Model: Combining the Advantages of Agent-based and Equation-based Approaches (see also Heterogeneity and Network Structure in the Dynamics of Diffusion: Comparing Agent-Based and Differential Equation Models by Rahmandad and Sterman 2006)

Abstract
Agent-based models (ABMs) are powerful in describing structured epidemiological processes involving human behavior and local interaction. The joint behavior of the agents can be very complex and tracking the behavior requires a disciplined approach. At the same time, equationbased models (EBMs) can be more tractable and allow for at least partial analytical insight. However, inadequate representation of the detailed population structure can lead to spurious results, especially when the epidemic process is beginning and individual variation is critical. In this paper, we demonstrate an approach that combines the two modeling paradigms and introduces a hybrid model that starts as agent-based and switches to equation-based after the number of infected individuals is large enough to support a population-averaged approach. This hybrid model can dramatically save computational times and, more fundamentally, allows for the mathematical analysis of emerging structures generated by the ABM.
Details: http://www.brookings.edu/papers/2007/winter_hybridmodel_epstein.aspx

Is this a step towards on how to verify and validate agent-based computational model? (see also Empirical validation of agent-based models)

Abstracts from the Geospatial Analysis session at RGS-IBG Conference

2008-09-04T08:58:00.007+01:00

The "Geospatial Analysis: GIS & Agent-Based Models" session at the RGS-IBG Conference included 5 interesting and ongoing research works which are summarized below.

Note:It is also expected to run the Geospatial Analysis: GIS & Agent-Based Models session next year - RGS-IBG 2009.

ABSTRACTS

Modelling Perceptions of Street Safety to Increase Access to Public Transport; Claire Ellul, Ben Calnan (Cities Institute, London Metropolitan University)

In the context of public transportation, “the provision of a permeable public space contributes to an inclusive journey environment” (Azmin-Fouladi 2007). However, when planning or modelling an urban environment, architectural vision and planning principles often take precedence over the way buildings and urban features make people feel. In particular, the identification of specific urban features that contribute towards a feeling of safety and security is not generally considered.

Our research aims to redress this imbalance by providing planners and local authorities with the means to identify potential barriers to the permeability of public space. It is argued that the removal of negatively-impacting features and the resulting increase in perception of safety will increase the use of public transportation.

We present two key outputs of this process. Firstly, we have developed an Index of Permeabilility (IoP) for the urban environment, where each relevant urban feature visible from a specific location has been assigned a weighting (through a process of consultation). This weighting contributes towards the overall index of permeability for the point. Secondly, we present a GIS-based implementation of this index using Isovists (which identify the urban features visible from a specific point), extending the index to create a surface of permeability.

System wide cultural districts: mapping and clustering the tangible and intangible cultural assets for the policy design of the regional clusters in the Veneto Region, Italy; Pier Luigi Sacco, Guido Ferilli (IUAV University), Massimo Buscema, Terzi Stefano (Semeion Research Center)

In previous research carried out by Sacco et al. the notion of system-wide cultural districts has been introduced and analyzed. In particular, system-wide cultural districts are horizontally integrated local clusters of economic activities in which culture plays a key strategic role as a social activator of innovative processes and practices, as well as an attractor of talent and resources, a factor of social cohesion and of networking, and of course as a sector with its own value added.
In other, related research from the same group, Artificial Neural Networks (ANN) techniques have been adopted to investigate to what degree they were able to single out emergent industrial districts of various kinds in selected areas of the Italian territory.
In this paper, we combine this two strands of research in a project carried out under the initiative of the Veneto Region, one of Italy’s outstanding productive regions. In the first phase of the project, the spatial distribution and clustering of all cultural activities and facilities with a non-occasional character has been mapped. This has led us to identify qualitatively a certain number of emergent culture-driven clustering. In the second phase, a battery of innovative ANN techniques has been employed to identify the ‘centroids’ of the cultural clusters and to check to what extent they overlap with the poles of the Region’s overall productive systems.
Finally, an analogous analysis has been conducted for specific cultural sectors – visual arts, performing arts, museums, and so on, to investigate to what extent they tend to gravitate upon specific cultural clusters and to what extent they are useful to define prospective local specializations by means of a specific policy design process.

Revealing the fuzzy geography of an urban locality; Richard Flemmings (Blom Aerofilms Ltd & Birkbeck University of London)

The delineation of urban geographical boundaries can be problematic, particularly when unitary authority boundaries do not represent perceived reality. The lack of agreement between perception and the reality of political boundaries, make an urban locality a fuzzy geography. This fuzzy geography can be exploited, for example by estate agents who wish to alter an area to increase property values. By giving such fuzzy boundaries definition, better clarity can be achieved between estate agent and customer.

A method is proposed here that gives definition to the boundary of an imprecise region using the internet as the information source. Kernel density estimation is used to transform geo-tagged internet search results into a continuous surface. This is both compared and combined with a kernel density estimation of relevant Ordnance Survey MasterMap® cartographic text labels. A composite Index of Urban Locality is given to represent the fuzzy boundary of Clifton, Bristol. The resulting continuous surface is graded based on membership. Thus, the extent that a location is within or is not within the urban locality is depicted. The success of this output has been verified using estate agent’s interpretations of the boundary of Clifton. The Index of Urban Locality has also been applied to the region of Bedminster, Bristol, with some success.

Geospatial Modelling and Collaborative Reasoning of Indeterminate Phenomena: The Object-Field Model with Uncertainty and Semantics; Vlasios Voudouris (London Metropolitan Business School & City University London)

The need for a conceptually unifying geospatial data model for the representation of geospatial phenomena has already been acknowledged. Recognising that the importance of the data model employed by and large determines what can be done by way of analysis and the methods by which the analysis can be undertaken, there has been some activity in developing unifying data models for geospatial representation in digital form. Some successes have been reported. Nevertheless, progress has been slow, especially at the conceptual and logical levels of abstraction of geospatial data models.

Concepts and ideas from cognitive and perceptual psychology as well as GIScience and GISystems literature are examined within the context of geospatial data modelling and reasoning. Drawing on and combining these concepts, ideas and successes with an empirical approach, this work presents the fused Object-Field model with uncertainty and semantics at the conceptual and logical levels of abstraction.

The purpose of the Object-Field model is to better support the representation and collaborative reasoning of geospatial phenomena, particularly indeterminate phenomena such as town centres. It is shown that many of the concepts required to better represent geospatial phenomena can be derived from a single foundation that is termed the elementary-geoParticle. This serves as the standard for integrating the dual continuous-field and discrete-object data models by means of aggregation

GIS and Built Form: Using Pattern Recognition for Energy Efficiency Models; Donald Alexander, Simon Lannon, Orly Linovski (Cardiff University)

Much of what has been written about residential development in the UK relies on anecdotal evidence (Whitehand and Carr 1999). Little ‘on-the-ground’ research has been conducted due to the significant time required for investigating development through building records and other municipal data. A wide variety of research often requires detailed building information that has previously only been obtainable through walk-by surveys or building records. This paper examines alternative methods for determining building age using pattern recognition algorithms.

This model has wide ranging applications including researching urban development patterns, conducting urban design studies and assessing energy efficiency. This paper specifically focuses on the use of building data for energy efficiency studies. Modelling software has been developed to quantify energy emissions but requires detailed information of the built environment and age of buildings (Jones et al. 2000). It is proposed that pattern recognition algorithms can be used to automate the collection of this data from GIS and aerial photos.

To develop this technique, two study areas in Wales were chosen as case studies. These areas were surveyed manually to establish a baseline for assessing the built form characteristics of each development that could be incorporated into the algorithm. This paper will present the results of the development characteristic study, as well as the efficacy of using these to determine the age of dwellings.

Towards a General Field model and its order in GIS

2008-08-30T16:26:00.003+01:00

An interesting paper has been published in the IJGIS by Y. Liu, M.F. Goodchild, Q. Guo, Y. Tian, and L. Wu title: Towards a general field model and its order in GIS. This is very closely related with the works of Cova and Goodchild (2002) and Kjenstad (2006) and with my PhD work which has been reported in Voudouris, Wood and Fisher (2005), Voudouris, Fisher and Wood (2006), Voudouris and Marsh (2007) and Voudouris (2008) - the full PhD will be made available soon after the PhD viva examination.

Abstract
Geospatial data modelling is dominated by the distinction between continuous-
field and discrete-object conceptualizations. However, the boundary between
them is not always clear, and the field view is more fundamental in some respects
than the object view. By viewing a set of objects as an object field and unifying it
with conventional field models, a new concept, the General Field (G-Field)
model, is proposed. In this paper, the properties of G-Field models, including
domain, range, and categorization, are discussed. As a summary, a descriptive
framework for G-Field models is proposed. Then, some common geospatial
operations in geographic information systems are reconsidered from the G-Field
perspective. The geospatial operations are classified into order-increasing
operations and non-order-increasing operations, depending on changes induced
in the G-Field’s order. Generally, the order can be viewed as an indicator of the
level of information extraction of geospatial data. It is thus possible to integrate
the concept of order with a geo-workflow management system to support
geographic semantics.

The paper can be downloaded from:
http://www.geog.ucsb.edu/%7Egood/papers/451.pdf

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Refrences
Cova, T.J. and Goodchild, M.F. (2002) Extending geographical representation to include
fields of spatial objects. International Journal of Geographical Information Science, 16,
pp. 509–532.

Kjenstad, K., (2006) On the integration of object-based models and field-based models in
GIS. International Journal of Geographical Information Science, 20, pp. 491–509.

Voudouris, V (2008) Geospatial Modelling and Collaborative Reasoning of Indeterminate Phenomena: The Object-Field Model with Uncertainty and Semantics. Pesented at the RGS-IBG International Conference 2008.

Voudouris, V., Marsh, S., (2007) Geovisualization and GIS: A Human Centred Approach. In Visual Languages for Interactive Computing: Definitions and Formalizations (Eds, F. Ferri), Idea Group Inc.

Voudouris, V., Fisher, P.F., Wood, J., (2006) Capturing Conceptualization Uncertainty Interactively using Object-Fields. In: Kainz, W., Reid, A., Elmes, G.(2006) (Eds). 12th International Symposium on Spatial Data Handling (Vienna, Austria). Springer-Verlag.

Voudouris, V., Wood, J., Fisher, P.F., (2005) Collaborative geoVisualization: Object-Field Representations with Semantic and Uncertainty Information . In: Meersman, R., Tari, Z., Herrero, P., et al (Eds).On the Move to Meaningful Internet Systems OTM 2005, Lecture Notes in Computer Science (LNCS), Vol 3762, Springer, Berlin

IJGIS Valediction by Peter Fisher

2008-08-30T10:54:00.007+01:00

Peter Fisher's Valediction is very interesting and promising for those who work in the area of geospatial data modelling (or representation) as It is the foundation of all else that is possible or can be done.

A pdf version of Fisher's Valediction can be accesses from http://www.informaworld.com/smpp/content~content=a784379045~db=all~order=page or you can read the html version below:

IJGIS Valediction

1. Introduction
I have been editing the International Journal of Geographical Information Science for the last 14 years. I was first associated with the journal in this role in 1994 during the publication of volume 8. Then, it was publishing 6 issues per year with a target length of 600 pages, which allowed approximately 30 articles to be included. In 1996, this increased to 800 pages, and in 2005 to 1200 pages. The journal now carries nearly 60 articles per year, and will have a modest increase in volume again next year.

I took over the journal from the capable hands of Professor Terry Coppock, who worked diligently to establish the journal as one which would persist and become the premier journal of record for those working on the development and application of geographical information systems, whatever their background. I have endeavoured to maintain the status of the journal, and I believe that I have. Elsewhere (Fisher 2007b), I have listed some of the competitor journals of IJGIS. IJGIS is unusual because it is listed in the Journal Citation Rankings of ISI's Web of Knowledge in four subject areas (Geography, Physical Geography, Information Science, and Computer Science). Among competing journals, IJGIS has had the highest impact factor over a number of years.

Volume 11 saw the journal published under its present title, when the name was changed from IJGISystems to IJGIScience, in recognition of the fact that the journal had always been engaged in the publication of research into the science of geographical information which underpins the systems that are in widespread use.

2. A personal view of research published in IJGIS
The research that has been published in IJGIS over the years can be divided many ways, but I choose to look at it as is illustrated in figure 1. These are the themes I see which have persisted through the 14 years. The structure identified here was first articulated at a presentation at the AGILE 2007 Annual conference in Aalborg. I would like to thank the organizers (including Lars Bodum and Monica Wachowicz) for inviting me to give that presentation.

Figure 1. Personal view of general research topics published within IJGIS.

To me, the most important research theme is that of Representation. It is the foundation of all else that is possible or can be done with geographical information. I view it as having five components:

Spatial Information Theory addresses how we conceptualize spatial information, and is absolutely central to GIScience. It has been a persistent theme with issues of RESELS, geoatoms, object orientation, and multiscale and multiresolution information as part of it.
Issues of Uncertainty in its broadest sense may be the most common research topic published in IJGIS. This includes probabilistic and fuzzy formalisms, error modelling, rough sets, and semantic uncertainty, among others.
Researchers have long bemoaned the lack of Temporality in geographical databases, but over the 14 years, many papers have been published in this area.
IJGIS has not been slow in publishing the results of Ontological research both from a database construction point of view and from a semantic understanding point of view.
Finally, and perhaps a smaller component than is desirable, is the research on Geometric representation.
The second broad topic is modelling, which, for convenience, I divide into:

analytical and statistical modelling, including network modelling and spatial statistics; and
process modelling, including modelling of social and environmental processes and the technology of those models.
Visualization has always been a major theme within GIScience, and of course, cartography, and computer cartography in particular, is one of the antecedents of the field. Many interesting papers and special issues have been published on topics from this field, including generalization, visual analytics, geocollaboration, and interactive mapping.

Cognitive studies and usability are concerned with how we relate to the world and the information about that world. There are increasing studies on usability, but my personal view is that studies on spatial cognition should ground much research in GIScience but have not been published in IJGIS, with a very few exceptions. I hope that the future may see more such research linking these areas.

A final persistent theme has been that of data policy with which I bracket social construction of information. The first has been researched in many ways and most recently within the umbrella of Spatial Data Infrastructures. The social construction argument is seen by some as anti-scientific, but in my view it is part of all information, as some recent studies have demonstrated, and those studies have shown some potential for working with different world views within GIScience.

Over the 14 years, paper submissions on some research topics have ceased. Parallel processing on which a special issue was published in Volume 10 has become a low-level system issue, with barely a mention of the topic in more recent issues of IJGIS. Similarly, Interoperability was the topic of a special issue in Volume 12, but it too has not been addressed directly in much writing in IJGIS since. The topic remains important, but within the research published within IJGIS, it has been subsumed within the interoperability of data, or within the developing area of Web technologies. Another person might see papers on Web technologies as another emerging component of the IJGIS research literature, but currently I see the Web issues as one that touches many of the other topics raised, particularly Visualistation and Data Policy.

Discussion of the structure outlined here has led others to suggest to me that the World Wide Web, Location Based Services and Global Change are so called 'killer applications' for GIScience, and so might be viewed as themes for structuring the field. These are all interesting areas for research with their own challenges and problems. However, I would rather see these as important areas for application, along with many others, rather than as driving forces. I believe that when an application becomes a driver, it moulds the science, and I do not believe that all applications will fit one mould. Therefore, it is necessary to keep the independence of the core issues as central to GIScience, and not view GIScience as issues of any one application.

3. Issues in producing the Journal
Many issues could be mentioned in the production process, but two stand out for me. The first is with respect to reviewers, and the second is the preparation of graphics.

3.1 Problems with reviewers
The most intractable problem in managing any peer-reviewed journal is making timely decisions on articles. This process is a trade-off between the need for reviewers to have time to read a paper, and an author's wish to have a rapid response, as well as the editor's wish to 'have a life' and do some of their own research. The most frustrating part of managing the process is that reviewers repeatedly promise to complete a review within a particular time period, but fail to do so. This can be for understandable reasons, but when the reviewer then promises to do the review by some new date but fails, and promises again and again, the process becomes very frustrating for everyone.

When I first started editing, a member of the editorial board said to me 'I hope to complete three reviews for every paper I publish'. 3 to 1 is the minimum ratio of reviewed to published papers to which all active researchers should commit. Because of rejections, unfortunately the ratio actually needs to be considerably higher. Unfortunately, there are people who will never return a review, no matter how many times they promise, and there are others who will always return a review, once they have said they will. Research productivity and administrative responsibility is no indicator as to group membership—some of the busiest people are the most reliable. But if you publish one paper, you should commit to reviewing at least three papers, and you should do them as if you were the author, in a prompt and timely manner.

3.2 Problems in graphcs
Authors should be more careful in their design of graphics, graphs, and maps. Perhaps the worst are the graphs generated in modern spreadsheets. One particular spreadsheet package uses grey backgrounds so that graphs are highly visible on the screen, but when these are printed, the grey tends to obscure the actual graph, as do such ephemera as the grid lines and oversized point markers. Unfortunately, many authors seem to be ignorant of the design guidelines of Edward Tufte (1983), which should be studied with care by all involved in illustrating scientific articles. Authors should be prepared to make multiple changes to graphs in the process of preparing an article, using smaller symbols and clear, white backgrounds. Similarly, many authors use grey fills for boxes in flow diagrams. On the whole, these are completely redundant and only obscure the text within the boxes. Boxes should be white, with the outline used to code the boxes, if that is desirable.

In the print technology used by the publishers, colour continues to be expensive, but colour in the electronic version of papers is free. This means that as much colour as an author wishes to include can be carried in any article, but the print version of that article may include all those graphics in greys. The problem with this is that many colours will produce the same grey, so that if information is colour-coded, but the print version is in grey, the coding may not carry over. Therefore, authors need to continue to be be careful in their use of colour and, where necessary, may need to consult experts in the use of colour.

4. Is it still research?
There are many interesting and challenging research topics to be addressed in GIScience, but there are some topics which might be considered to be pass for publication in IJGIS. Without wishing to put off researchers, I would like to mention two here.

First is the annual assault on the editors of papers documenting yet another instance of a raster-GIS implementation of the Universal Soil Loss Equation (USLE), by authors who have not read the literature well enough. This topic was first addressed in the 1980s by, for example, Spanner et al. (1983), and papers being submitted in 2007 are very little different. I am not saying that soil erosion modelling is pass, but as scientific research the USLE is, both within and outside GIS. On the contrary, research relating to more advanced soil erosion models is welcome, and excellent contributions have been included in the Journal, when they meet the review standards.

Similarly, many articles have been written on comparisons of a modest number of surface interpolation algorithms in an experimental situation (whether from point observations or contours and using IDW, spline, and kriging, perhaps). Papers continue to be submitted doing no more. It is easy to conceive of such an experiment, but it is a real challenge to make it original and different from previous experiments, and to demonstrate that the conclusions can be generalized to other contexts. Generation of digital elevation models is no longer dependent on the interpolation of values from sparse point observations or contour lines, but has moved over to measurement-based remote sensing devices such as Lidar and Ifsar. Interpolation remains important for these technologies, but the issues have changed. Future experiments need to be demonstrably relevant.

5. Thanks
During the 14 years I have been working on the journal, I estimate that about 625 papers will have been published, which means that something of the order of 1800 papers have been submitted. A number of people in various roles have been involved, and I would like to record my thanks to them all (in spite of having listed many in a previous acknowledgement; Fisher 2007a):

First are those people whose work has been published in the journal over the last 14 years. I thank them for taking the time to conduct the interesting research they have submitted, and for writing it up. Almost without exception, they have taken criticism from reviewers and papers have gone through changes in the review process. We believe that the published papers which result are better than those originally submitted, but making the changes can be nonetheless painful for the authors. It has been a pleasure for my colleagues and I to see this work through the review process.
Because each paper is sent to at least three reviewers, approximately 5400 requests for reviews have been dispatched. I am ashamed to say that I have no idea how many reviewers this equates to, because I do not know how many have been asked more than once, although I suspect it is the majority. My thanks go to all those who have responded with reviews, when requested. The work involved in taking time and care to consider and critique the work of others cannot be understated, but it can also be most rewarding. Foremost among these reviewers have been members of the editorial board.
All journals have two classes of author: authors whose work is accepted, and those whose work is rejected. The acceptance rate is approximately 30% of submissions, and therefore the latter group is about twice the size of the former (except that, of course, some authors are in both categories), and having taken the effort to conduct the research and write the paper to then have it rejected for publication is always very dispiriting. These are the unacknowledged facilitators of the peer-review process, and I would like to take this opportunity to thank them all, because their work has come to nothing and will not be published in this journal.
During the 14 years, 16 special issues have been published, and a number more are in preparation. The editors of these issues are numerous, but they are acknowledged by being the authors of guest editorials.
It has been my pleasure to work with a number of other people in editorial roles, including Eric Anderson, Steven Guptill, Marc Armstrong, Harvey Miller and now Mark Gahegan as North American Editors (now Editor for the Americas), and Dave Abel and now Brian Lees as editors for the Western Pacific (now Editor for Australasia and Eastern Asia). I have worked with Neil Stuart, Nick Tate, and Lex Comber as Book Review Editors.
Throughout my period as editor, the Publisher's principal representative has been Richard Steele. Direct managerial contacts for the journal have been Meloney Bartlett, Rachel Sangster, and Virginia Klaessen. On the production side, managing the work of anonymous typesetters and copy editors, are the people with whom authors have communicated about proofs (whether they know it or not). They have been David Chapman, Sophie Middleton, Heidi Cormode, and currently James Baldock.
Finally, I must thank Jill Fisher, who has given continuing support and assistance in communicating with authors and reviewers.
The system of peer review, which is the current paradigm for scholarly publication, would not work without all these players; all are crucial to the process. My thanks to all these people in their various roles, from reviewers and authors, to editors and production managers, and to anyone else I should have named but have not. The last 14 years would not have been possible without each and every one of you.

I would like to close by offering my very best wishes for continuing success of the journal to the future editorial team, including Brian Lees (Australian Defense Force Academy, University of New South Wales) as both Editor in chief and Editor for Australasia and Asia, Mark Gahegan (Pennsylvania State University) as Editor for the Americas, and Sytze de Bruin and Monica Wachowicz (Wagenigen University) as Editors for Europe and Africa. I hope that they find it in as good condition as Terry Coppock left it for me.

References
1. Fisher, P. F. Fisher, P. (ed) (2007a) Preface.. Classics from IJGIS: Twenty Years of the International Journal of Geographical Information Science and Systems pp. v-vi. Taylor & Francis , London
2. Fisher, P. F. Fisher, P. (ed) (2007b) 20 years of IJGIS: Choosing the classics.. Classics from IJGIS: Twenty Years of the International Journal of Geographical Information Science and Systems pp. 1-6. Taylor & Francis , London
3. Spanner, M. A., Strahler, A. H. and Estes, J. E. (1983) Soil loss prediction in a geographic information system format.. — In Papers Selected for Presentation at the 17th International Symposium on Remote Sensing of Environment. Volume 1, pp. 89-102. (Environment Research Institute of Michigan, Ann Arbor.)
4. Tufte, E. R. (1983) The Visual Display of Quantitative Information Graphic Press , Cheshire, CT

Unlocking Economic Systems with Agent-Based Computational Economics: The EU Leasing Market

2008-05-01T10:06:00.003+01:00

THis is the work that I (with Haris) present at the RGS-IBG International Conference 2008.

Studies of economic systems must consider how to handle interdependent feedback interactions of micro behaviors, interaction patterns and macroscopic regularities. The Agent-Field framework is an approach for agent-based computational economics. In this framework, models of economic systems are viewed as a collection of multi-scale and structured agents operating in indeterminate economic environments conceptualized as continuous, differentiable fields with variable levels of spatial uncertainty. We propose formalization of the Agent-Field framework using the Unified Modeling Language. We explore potential advantages and disadvantages of the framework for the study of economic systems using the EU leasing market. This enables us to formulate an initial frame representation of major economic agents for the EU leasing market. We predicted the direction of the Central and Easter cluster of Europe's high growth economies can be expected to take, as its economies move towards higher prosperity levels. Within the scope of the work, it has been shown that the Agent-Field framework is an intuitive rather than an abstract process in modeling economic systems. This intuitive process needs more understanding of the interactions between the economic environment and the agents within it. The Agent-Field approach seems ontologically well founded for the growing field of agent-based computational economics.

Geospatial Analysis: GIS & Agent-Based Models

2008-04-07T01:27:00.003+01:00

This year I organise the Geospatial Analysis: GIS & Agent-Based Models session at the RGS-IBG Annual Conference 2008 in London. We hope that the session will attract interest from users of GIS and Agent-based models for the analysis of geospatial phenomena, and particularly those who are interested in the fusion of these two areas. The deadline for submission to this session is 17th April 2008. Abstract should be sent to v.voudouris@londonmet.ac.uk

On the Integration: GIS with Agent-Based Models

2008-02-03T20:43:00.002Z

ArcGIS now interacts with Repast using the Agent Analyst:

The Agent Analyst is a free and open source ArcGIS extension that allows ArcGIS users to build geographically aware agent-based models. Agent Analyst achieves this goal by integrating the free and open source Recursive Porous Agent Simulation Toolkit (Repast) into ArcGIS (see here for details).

This offers interesting opportunities for both the agent-based community (see Batty, 2005) and GIS community (see Repast Vector GIS Integration for details). In my PhD thesis, i am suggesting a way of integrating agent-based wIth GIS using the object-field model (details will be posted soon).

Reference
Batty, M (2005), 'Approaches to Modelling in GIS: Spatial Representation and Temporal Dynamics'. In Maguire, Batty and Goodchild (eds.): GIS, Spatial Analysis and Modelling, ESRI Press

2008-02-03T20:43:00.001Z

Agent-Field Economic Model

2008-02-03T05:54:00.000Z

Recently, I have done a work about Agent-Based Computational Economics and the Object-Field model as a novel way to explore Economic Systems. This is my (with Haris) proposal:

Economies are complex adaptive systems encapsulating micro structures and behaviors, interaction patterns, and macroscopic regularities. Thus, studies of economic systems must consider how to handle interdependent feedback interactions of micro behaviors, interaction patterns and macroscopic regularities.
One such approach is the Agent-Field approach of the agent-based computational economics. In this framework, models of economic systems are viewed as collections of multi-scale and structured economic agents from the real world such as individuals, social groupings, institutions and physical entities and as smooth, continuous economic environments called fields. In other words, the Agent-Field framework is a fused agent-based model by capturing agents in indeterminate economic environments conceptualized as continuous, differentiable fields with variable levels of spatial uncertainty and embedded semantics. The science of the Agent-Field model is drawn from the field of Geographic Information Science (GIS) models (particularly the Object-Field model) and the field of Agent-Based Computational Economics. Thus, a common base-model for the Agent-Field framework is proposed by giving it a formal definition using the Unified Modeling Language (UML). We explore potential advantages and disadvantages of the Agent-Field framework for the study of economic systems using the EU leasing market economy as an example of demonstrating the application of the framework. This also enables us to formulate an initial frame representation of major agents and smooth, continuous economic environments for the EU leasing market (leasing being one of many ways in which businesses finance their capital investments). Each national leasing market can be viewed as an agent, with a range of particular internal dynamics that gives it specific character (e.g. preference of national businesses in the use of leasing over time, expectation for future economic growth, attitudes towards other forms of financing investments etc). At the same time, a number of exogenous 'forces' also have an effect over each agent: forces such as the evolution of other national economies in close proximity, cross-border economic activity, pan-European taxation/regulation changes etc. By studying the leasing penetration in each national market (defined as the ratio of new yearly leasing volumes by the total yearly fixed capital formation in each economy) and comparing them with a measure of each economy's overall wealth (e.g. GDP per capita), Europe's national leasing markets fall into three clusters of agents: the first includes economies that are both large and wealthy (viewed in GDP/Capita terms) with a mature leasing market reaching high penetration levels. The second cluster includes economies that are wealthy and mature, but show very low leasing penetration levels. A third distinct cluster includes broadly the new EU entrants, i.e the smaller but high growth economies of Central and Eastern Europe, characterised by low GDP/Capita levels and at the same time exhibiting high leasing penetration levels. An Agent-Field model can be developed to map the dynamics that drive each cluster of economies, so as to help predict the direction that the third cluster of Europe's high growth economies can be expected to take, as its economies move towards higher prosperity levels. Within the scope of the work, it has been shown that the Agent-Field approach appears to be an intuitive rather than an abstract process in modeling economic systems. This intuitive process needs more understanding of the interactions between the economic environment and the agents within it as these elements represent the logic underlying the problem at hand rather than mathematical notation. The Agent-Field approach seems ontologically well founded for the growing field of agent-based computational economics.

Intelligent Memory: Understanding the conceptualization process

2008-01-16T20:15:00.000Z

Myself, with Jo Wood and Peter Fisher, discussed the idea of conceptualization uncertainty in the SDH2006. We argued that conceptualization uncertainty is introduced during the conceptualization of a phenomenon rather than due to measurement error.

Barry Gordon, professor of neurology and cognitive science, presented the concept of Intelligent Memory which is the mostly unconscious, lighting-fast thought process that connects pieces of memory and knowledge in order to generate new ideas. It's the memory that aids us in making everyday decisions, gives us the chuckle of a good joke, sparks a "Eureka!" solution to a problem, and enables us to enjoy a work of art. Intelligent Memory is what powers most of our mental life.

I personally think that understanding intelligent memory can give us some ‘clues’ about how people conceptualise and argue about indeterminate phenomena such as town centres.

The Science Behind Intelligent Memory

What follows is an in-depth explanation of the neuroscience behind the Intelligent Memory concept. It's for readers who want to understand the scientific underpinnings of memory and learning.
All memories, along with every perception, action and thought, arise from the activity of nerve cells. However, the memories that we are conscious of and which are important to us, obey somewhat different rules than nerve cells. This is makes sense, given that our important memories generally require the coordinated action of thousands, if not millions, of nerve cells.
Paradoxically, some of these nerve cells help generate memories by not being active or not communicating with other neurons. They're somewhat like the essential patches of blank canvas that an artist uses to suggest clouds or a piece of reflected light. Another analogy can be found in the printing that you are reading at this moment. The letters and the words the ink forms are meaningful because of where the ink is, and is not.
The key to memory is time. In essence, memory is a displacement of knowledge a little bit into the future. Or, from a future perspective, it's the retrieval of knowledge from the past. This knowledge can be latent, or unused, or active and available. When nerve cells are firing, they are actively carrying information, and so the memory is active, and usable.
But this form of memory is also transient and by itself, it can exist only a few fractions of a second. What makes a memory permanent is not a nerve cell constantly firing but over time acquiring more potential for being able to fire. In other words, a nerve cell becomes more sensitive to firing or to staying quiet. This sensitivity to being triggered into action can be varied up or down. The processes that change susceptibility are built into nerve cells. There are many of these processes, including temporary changes in the permeability of the nerve cell membrane and permanent changes within its DNA. Correspondingly, they can take place over different time scales. Changes in the permeability of the nerve cell membrane can occur in fractions of the second, while changes in the proteins within a nerve cell may take hours to days to generate. And DNA may take weeks to months or years to change.
One of the crucial contributors to nerve cell sensitivity is individual experience - whether and how often they've fired before. If a nerve cell has been triggered to fire in the past, in general it will be more sensitive to those triggers in the future. Yet, if a nerve cell has been active over long periods of time, it gradually becomes less sensitive and needs increasingly more stimulation to set it off or produce changes.
Oddly enough, this intrinsic regulation is basic to creating intelligent memories. This regulation, when repeated over and over, produces particular kinds of memories - memories that arise through practice. Repeating a thought or action strengthens and weakens individual connections between nerve cells, and the upshot of many connections is learning. By and large, this learning happens relatively slowly. It takes a fair amount of repetition to convince nerve cells to be more sensitive the next time. Doing something once doesn't do it. Doing something twice or three times doesn't do it. But doing something hundreds or thousands of times definitely does.
You know these kinds of memories well. They are the memories you acquire when you learn how to ride a bicycle, to drive a car, to play golf or to add 2 + 2. As you acquire them, you can strengthen them quickly if each time you think about the precise right way and immediately correct your mistakes. However, if a task is complicated, you need a great deal of practice.
Although so far the focus has been on individual nerve cells, keep in mind that most of the memories and activities that mean anything to us take long chains of nerve cells. Catching a ball requires chains for seeing as well as chains for hand control. Nevertheless, individual nerve cells and connections between them are the basis for these activities.
Getting back to how nerve cells form memories and learn: on their own, individual nerve cells don't decide whether to learn. Brains as complex as ours have additional circuits of nerve cells that monitor what's important and what needs to be repeated and remembered. Such circuits control how other neural circuits learn. They can even force neural circuits to learn quickly. ("Enough daydreaming - remember this!") Or, they can stop them from learning at all. These control circuits also dictate how the more basic neural circuits are wired together, which get inputs and which do not, and which chains of circuits are beefed up and which are broken up and rewired.
And, as you may have guessed, our brains also have circuits that monitor and control the controlling circuits. And there are undoubtedly monitors and controls for the monitoring and controlling circuits, and so forth. Neuroscience doesn't completely know how many levels of controls our brains possess. They're hard to identify or track down because there is not a strict hierarchy. Instead, some controlling circuits seem to influence other controlling circuits at the same level and sometimes lower-level processes can boss around their controllers.
Our brain's basic wiring plan governs how we perceive, act, think, and remember. But to understand intelligent memories, we need to elaborate beyond this basic scheme and look at the links between nerve cells and nerve circuits. It's these connections which are the true building blocks of thoughts, and Intelligent Memory. ("Intelligent Memory" is our shorthand term for all the different intelligent memories. They all work much the same way; it's just their specific contents - such as words or images - that differ.)
What we think of as a single thought in our mind - "ball" for instance - is composed of many fragments of thoughts. If you think about a ball, you do not normally separate its color from its roundness or its bounciness. However, your brain does. Its color and shape and function are stored in different regions of the brain, although not every distinct element has its own region.
In the brain, these elements of thought are represented by patterns of activity in many nerve cells. These patterns can be active and the nerve cells firing, or they can be latent, existing in the pattern and strengths of connections between sets of nerve cells. An idea in our mind -- whether it's the color or the shape or movement of a ball -- is represented in the activity or latent activity of these sets of nerve cells as a whole. And thoughts that we are very interested in are likely to involve thousands if not tens of thousands or more nerve cells.
Most complex thoughts have to be learned; they are not innate. When elemental thoughts arise from the senses, its usually constant exposure, like playing with balls as a child, that gradually produces the whole idea inside our minds. The same process seems to be at work for thoughts or concepts that have no obvious sensory or other correlates.
Elements of thoughts are linked in many ways. Sometimes they are linked just by being part of the same entity in the outside world, as in the case of the ball. In this case, there are linked by experience. But the most interesting links for our purposes -- the links that make up intelligent memories -- are ones we discover and put into place. They are the links, for example, that allow a child to see the similarity between the ball he is throwing and the planet he is standing on.
The links between elements of thoughts, or between thoughts themselves, are patterns of neural activity, either active or latent. Therefore, they can be learned.
Links between thoughts produce thinking. Some kinds of thinking generated by these links may seem so ordinary that we don't call it thinking at all. Being hungry, passing a candy machine, and stopping to put in a coin is hardly a Nobel prize-winning connection. But even these thoughts required having the elements inside of our head (some coming internally, from our hunger; others coming externally, from the image of the candy machine) and then making the connection between them. (It also involved acting upon that connection.)
Solving harder, more complex problems requires more and better connections. But this should not obscure the fact that elements of thought and the links between them are nevertheless necessary. Moreover, it is easy to understand that creative thinking occurs when the links go in unpredictable directions or towards goals we did not set in advance. But they are still links, and they still arise from the same nerve cell activity and the same learning process.
Links are the streets that take us from thought to thought. But finding connections between thoughts, or finding the best ones, can be like trying to find the best route to a destination. The first route we explore may have many false starts or roads that look good on paper but don't work in practice. With time, though, we find a shorter work or faster route. So it can be with thinking. Over time, we can prune away the false starts and wrong directions, and eliminate the links that look good originally but prove to be rocky or laborious or time-consuming.  This process of finding the best mental route is the essence of training our thinking. But from the perspective of what nerve cells must do to be trained to think, it is also learning. Memory mediates mental training. This memory, this learning, is what helps make us intelligent. It's also a basis for intelligent memories.
Nerve cells also comprise the circuits that monitor the links and open and close the routes, and these, too, can learn and can improve. The controlling systems, these guidance providers inside of our heads, can be trained and so form another site for intelligent memories.  At least two more physical facts about memory and our brains figure into an understanding of our thinking, learning and creativity, and how they can be improved. One of them relates to how learning can be enhanced. The other relates to how we create miniature intelligences in our minds to help eliminate the bottlenecks of certain kinds of thinking.
Nerve cells learn when they are exercised. Practice, which stimulates connections, makes nerve cells learn. However, nerve cells also learn when we tell them to. When we deliberately activate the circuits that signal something is important, the circuits pass on the message and tell the appropriate other nerve cells that what is happening is important and should be learned well. This happens, for instance, with the learning involved in memorizing facts, names or faces.
While it is less clear that the circuits involved in learning connections between thoughts can be revved up this way, it seems almost certain that interest and motivation synergistically tickle nerve cells and make them learn much faster. So this is another mechanism we can use to enhance our Intelligent Memory.
The bottleneck mentioned earlier arises with our conscious thinking and attention. When we are consciously and fully alert, we can keep no more than a few thoughts in our mind at once. (Perhaps just only one thought at a time can be maintained consciously.) Our unconscious, automatic minds, on the other hand, do not have such a bottleneck or limitation. And fortunately, much of our mental activity takes place unconsciously and automatically. When you walk, you don't think about every irregularity in the pavement, or every curb you step on. Those perceptions, decisions, and actions are handled automatically and unconsciously.
Your mind did not always perform such mental tasks automatically. There was a time when you had to learn them. As an infant, you had to learn to walk, which required paying attention to the terrain in front of you and coordinating what you saw and felt to how your body reacted. A better example of the process may be when you learned how to drive a car.
When you learned to drive, you had to learn to pay attention to everything going on and everything you had to do. You watched your hands on the steering wheel, the hood of the car, each sign and traffic light, the other cars on the road, and every pedestrian. You also had to think about what to do in situations: the stop sign or the yield sign, a car getting too close, a pothole. But as you practiced driving and became better, your ability to detect what was happening on the road as well as your reactions became more automatic. You didn't have to consciously look for a stop sign or a red light in order to notice it and automatically respond the right way. And if a pothole suddenly appeared, you knew you would immediately see it and not only swerve but check your mirrors for other cars nearby and slow down.
What you did through all this practice and attention was create automatic mental abilities. You used your conscious mind and deliberate intention to instruct your brain on what to attend to, what decisions to make, and what to be done. Your conscious mind programmed the necessary circuits in your brain. It instructed your vision to pay attention to the color red on a light or a sign. In addition, your mind established a network of override circuits so that the need to stop would take precedence over almost everything else. It also set up a watchdog circuit, so you would not stop too quickly if a car was on your tail. Finally, it programmed what you have to do to stop: take your foot off the gas and push the brake pedal. All these mental processes had to be laid down and practiced to the point that they became instinctive, like a separate intelligence or "minimind" operating on its own.
Now that you are an experienced driver, this minimind is vigilant whenever you're behind the wheel, ready to respond to any stop sign or stop light. You don't have to think about it and it no longer requires your conscious attention. Because it's automated, it works in parallel with your conscious mind. It augments your abilities. It augments your intelligence.
Elementary mental processes are relatively rapid. They operate in hundreds of a second, or at their slowest, tenths of a second. However, these elementary mental processes are often strung together in chains and loops and these strings of processes often take a fair amount of time to unfold. Conscious minds may need more than a second to appreciate a situation, and several seconds of backwards and forwards thinking to come up with a response. Our unconscious, automatic minds, on the other hand, are much simpler and more direct, and can work much faster. A baseball thrown by a professional pitcher moves too quickly from the pitcher's mound to the plate for a batter's conscious thought to react (which takes a minimum of 1/4 of the second). But the batter can preprogram his miniminds to watch the pitcher's throw and to watch the ball, so that his swing has a decent chance of connecting.
All of your thinking, all of your decisions, all of your creativity comes from the same kind of miniminds you apply to skillful driving. But these miniminds cannot always substitute for careful, deliberate thinking. Sometimes, the information they use is too limited, and the judgments they make are too quick. Still, they augment the powers of your conscious mind, which usually does not have the luxury of unlimited evidence and slow, deliberate thinking.
These miniminds, which represent intelligent memories, take time to be constructed, but they are extremely persistent once they have been built. This is often an advantage, since a useful mental tool should be kept around. However, this persistence can also cause major problems. Problems can arise when a minimind has not been constructed properly or when its operation has taken wrong turn that becomes permanent. For example, making a snap judgment using these miniminds is a big reason people make errors on everyday problems, particularly those involving statistics and logical thinking.
A first step in enhancing your miniminds is to understand what types you have available. The ones that work well can be left alone, while the ones that repeatedly make mistakes need to be retrained. When you survey your mental abilities and needs, you may well discover that you need certain abilities -- miniminds -- that you do not currently have. These gaps need to be identified and filled, and to take their place alongside your high-functioning miniminds. And, of course, you need to train the intelligent memories that orchestrate these particular miniminds, so the right ones can be used in the right situations.
Now you know more of the details about why we can have Intelligent Memory, and why we can consciously exercise this memory and make it stronger.

Decision Making

2006-12-24T12:56:00.000Z

Mind Tools Newsletter presents 3 ways of decision making processes:

Multivoting

The democratic system of majority wins is usually a fair way to make a decision. So long as voters have sufficient information on which to make a choice, the system usually works well, just as long as there are only a few options from which to choose.But what happens when the choices expand and each vote is then dispersed over a wider range? A winner emerges but there are many more people who didnt vote for the winning option than people who did.

When there are many choices, simple majority rule voting is often not the best method for reaching decisions, if you want everyone to feel that they own the decision. Yet with idea sharing and brainstorming activities frequently taking place in workplaces today, voting is needed more and more. This is particularly the case where the decision is subjective, where different strong views are held, where many members of the group have power, or where strong commitment to the outcome is needed.

When group consensus is needed, multivoting is a simple process that helps you whittle down a large list of options to a manageable number. It works by using several rounds of voting, in which the list of alternatives becomes shorter and shorter. If you start with 10 alternatives, the top five may move to the second round of voting, and so on.

In addition, in all but the last round, each person has more than one vote, allowing them to indicate the strength of their support for each option. Everyone votes in each cycle, so more people are involved in approving the final outcome than if only one vote was held.

Multivoting helps group members narrow down a wide field of options so that the group decision is focused on the most popular alternatives. This makes reaching consensus possible, and gives an outcome that people can buy into.

Delphi Method
It’s a common observation to say that when you get three experts together, you’ll often end up with four different opinions. This is particularly the case in areas (such as resource allocation and forecasting) where the conclusion reached depends on a number of subjective assessments. Arguments can quickly become passionate, and disagreement can often become intensely personal and bitter.

More than this, in face-to-face discussion, situations of “groupthink” can occur. Here (for example) the eccentric views of early or charismatic speakers can achieve undue prominence as the group seeks to find consensus. This can lead to poor decision making.

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This is where a technique like the Delphi Method is needed to reach a properly thought-through consensus among experts.

The Delphi Method is a structured approach to problem analysis which makes sure that problems and proposed solutions are thoroughly explored and examined.

By using a remote and anonymous approach, it avoids the problems of groupthink and personality conflict that can lead to poor group decision making. More than this, it allows the time for detailed analysis and careful criticism that so often is not possible within a group analysis and decision making process.

The process works through a number of cycles of anonymous written discussion and argument, managed by a facilitator. The facilitator controls the process, and manages the flow and consolidation of information.

Nominal Group Technique
When a group meets, it’s often the case that people who shouts loudest, or those with higher status in the organization, get their ideas heard more than others. So when it comes to gaining consensus on important decisions or priorities, how do you make sure you get true consensus and a fair decision for the group?

One technique to help with this is the Nominal Group Technique, a face-to-face group process technique for gaining consensus. A typical application is in organizational planning when a group needs to agree priorities in order to assign resources and funds.

The benefit of the technique is that the group shares and discusses all issues before evaluation, with each group member participating equally in evaluation. The evaluation works with each participant “nominating” his or her priority issues, and then ranking them on a scale of, say, 1 to 10.

See http://www.mindtools.com/pages/main/newMN_TED.htm for details

Strategic intuition - Decision making

2006-10-29T01:20:00.000Z

Professor William Duggan from Columbia Business School presents a very interesting idea that builds on recent research on expert intuition which supports the notion that in urgent situations, people make decisions by combining analysis of past experience with a flash of insight. For example, in the 1990s psychologist Gary Klein studied the decision-making processes of emergency room nurses, firefighters and soldiers in battle. While these experts initially attributed their choices to intuition, further probing revealed that they were actually making rapid connections between the situation at hand and similar situations stored in their memories.

He also states that recent brain research provides further evidence that people make decisions through a combination of analysis and intuition. In 2000 a group of neuroscientists won the Nobel Prize for a new model of the brain called intelligent memory, which overturned the previous left-brain/right-brain model. “Basically as you go through life, you’re putting things on the shelves of your brain,” says Duggan. “The scientists call it parsing; it’s technically analysis. Your brain is constantly comparing what it’s taking in to what’s already there, and when it finds a combination — a synthesis — you have an insight.” These ideas are build on the four elements of Napoleon’s approach to strategy: (1) examples from history, (2) presence of mind, (3) a coup d’oeil or flash of insight, and (4) the resolution to move forward and overcome all obstacles.(see Strategic intuition: The key to innovation for details - checked 29 October 2006)

Object-Field by Watanabe & Nishio

2006-10-18T11:45:00.000+01:00

This is a very interesting article (The object-field model for managing a group activity) by Watanabe and Nishio.
Abstract
The object-oriented model is very applicable to represent various phenomena in the real world from an entity-interaction point of view. The successful modeling methods have been developed on many current topics. However, it is not sufficient to model a group of interrelated objects and dynamic actions of individual objects effectively under this paradigm. We introduce the concept of field in addition to the notion of object with respect to constructing the cooperative environment for objects, and then discuss our modeling method based on the object and field. Our modeling method is successful to construct a group of objects through the field, and to organize a hierarchical structure among objects by looking upon the field as an abstract object conceptually. Also, we explain a property adaptation mechanism of objects in fields with respect to the group activity of objects. (Access the article )

Poster at GIScience 2006

2006-10-14T13:02:00.000+01:00

This is the poster I presented at GIScience 2006. The poster forms part of a proposal to consider how the Object-Field model, a model that is considered to combine both the discrete-object and continuous-field views, has some unique qualities for collaborative decision making. It aims to prompt conceptual and theoretical thoughts and discussions by identifying when to use the Object-Field model and not the conventional object and field models . The insights and comments addressed the following kinds of questions :

1) Is the integration of knowledge and observational data useful & usable ?

2) Is the integration of Object & Field views brings new analytical advantages?

3) Is the Object-Field model an improved sharing of collaborative analysis means?
An answer?
The Object-Field model enables visualization and representation of objects in a field. This approach (visualization of field objects) could work better than the conventional approaches in a collaborative environment by presenting the users with multimedia objects, objects that record information in text, image or other forms, related to specific locations or a set of locations in a field. An object in this context is a modeller’s conceptualization/knowledge. Thus, the users are not presented with just observational data but this data is augmented by visualized users' conceptualizations/knowledge of geographic domains, and their understanding using embedded metadata expressed as semantic and uncertainty objects. These embedded semantics and uncertainties propose a new dimension in the metadata discussion. This is the explicit recording of collaborative understanding, interpretation and criticism.

In the Object-Field model, the four types of relationships between locations and objects enables the conventional one location-one object and many locations-one object relationship but it also enables the one locations-many objects and many locations-many objects. This mathematically-based relations between objects and locations enables the user to rearrange the objects in a way that supports comparisons. It also enables a object-based approach when operations are applied. Thus different conceptualizations and interpretations between collaborators can be easily visualized and cross-checked by reapplying them to new purposes or procedures

Integrating observational data and derived knowledge (expressed as metadata objects in the Object-Field model) enable us to improve sharing of analysis by designing a user interface that uses the same set of tools for the exploration of data and knowledge. This integration is important in cross-cultural collaborative environments as it manages semantic inaccuracies and making metadata not only useful but also usable.

Definitions of Object-Field, Field and Object

2006-10-14T12:47:00.000+01:00

The Object-Field view of the world is considered to combine of both the discrete-object and continuous-field representations. The Object-Field model attempts to integrate the field and the object view in a single, combined and integrated data model. This is achieved by mapping locations in a field to objects. Aggregating field locations forms the objects. ). This model uses a single elementary spatial unit (hereafter object element) to exploit the benefits of continuous-field and discrete-object views. The object elements are associated with a field value and a variable number of object references. See an Object-Field example by Cova & Goodchild (checked 14 October 2006).

the discrete-object view of the world is considered as a series of entities located in space. An object is a digital representation of these entities. Objects are classified into different object types such as point objects (stores), line objects(retail network) and area objects (London Boroughs). These Objects are defined by their boundaries. In turn, we attach/associate one or more attributes with these objects to specify what is located at these places. These general classes are instantiated by specific objects and, we can attach behaviours to these objects

The continuous-field view of the world is made up of properties varying continuously across space. The key factors of the field view are spatial continuity and self-definition. As the key characteristics of the field view is spatial continuity and self-definition we are not forced to identify objects and their boundaries. In other words, the field is a collection of a certain kind of measurements (such as consumer’s spending behaviour) that are used to define a value everywhere in the field and it is the values themselves that define that field.

My publications about the Object-Field Model, collaborative Visualization and Decision Making

2006-10-14T03:14:00.000+01:00

Voudouris, V., P. F. Fisher and J. Wood (2006) 'When and Why Object-Fields and not just Objects or just Fields?'. Presented at the Fourth International Conference on GI Science 2006 (Germany), Ifgi-Prints Series.

Voudouris, V., P.F. Fisher and J. Wood (2006) 'Capturing Conceptualization Uncertainty Interactively using Object-Fields' in W. Kainz, A. Reid and G. Elmes (eds) 12th International Symposium on Spatial Data Handling. Springer-Verlag.

Voudouris, V. P.F. Fisher and J. Wood (2006) 'Collaborative Visualization: Metadata within Object-Fields as Communication Means'. Presented at the RGS-IBG Annual International Conference 2006, London, UK.

Voudouris, V. and S. Marsh (2006) 'Geovisualization and GIS: A Human Centred Approach'. In Visual Languages for Interactive Computing: Definitions and Formalizations (Eds, F.Ferri), Idea Group Inc.

Voudouris, V., J. Wood and P.F. Fisher (2005) 'Collaborative geoVisualization: Object-Field Representations with Semantic and Uncertainty Information' in: Meersman, R., Tari, Z., Herrero, P., et al (Eds) On the Move to Meaningful Internet Systems OTM 2005, Lecture Notes in Computer Science (LNCS), Vol 3762, Springer, Berlin

Welcome

2006-10-13T21:24:00.000+01:00

The purpose of this blog is to promote discussions about the Object-Field model, applied statistics and mathematics, decision making, visualization, object-oriented modeling and knowledge representation.

From time to time, I will post my personal opinion about these issues based on my research and work experience.

Please post interesting ideas, links and articles about the data and knowledge modelling, applied statistics and mathematics, theory of decision making, visualization and object-oriented modeling.