Scott
Moss' Home PageI am the Professor of Social Simulation and Director of the Centre for Policy Modelling in the Manchester Metropolitan University Business School.
Although I trained as an economist, I was convinced from very early in my association with that discipline that its concentration on equilibrium states (including transient equilibrium states in dynamic models) is unlikely ever to be useful. I also concluded that the representation of agents by constrained optimization algorithms implied that agents always have sufficient computational and information processing capacity to calculate their optimal behaviour on the basis of available information. I was much more taken with bounded rationality as a condition in which agents' computational and information processing capacities are limited in relation to available information. This led me to adopt techniques from artificial intelligence as an appropriate (or, as my colleague Bruce Edmonds has it, credible) basis for representations of agents. Of course, economists are not interested in credible representations of agents and, so, I have had to conclude that my work is not what the economics profession would call economics.
My current views on economics and what should be done with it, especially in relation to business and public policy analysis, is set out in my inaugural lecture as Professor of Social Simulation.
One of my attempts to demonstrate to economists that it is both possible and fruitful to apply rigorous reasoning to historical (or as economists would have it, anecdotal) evidence was my 1981 book, An Economic Theory of Business Strategy. The book achieved some success with business historians but was almost entirely ignored by economists. The reason? I think it was that I applied an economics style of reasoning to questions of how markets actually work and why different markets function differently. In particular, inspired by business historians such as Alfred Chandler and the book Merchants and Manufacturers written by his former students, Glenn Porter and Harold Livesay, I argued for the importance of technology in exchange and how technology determines market forms. You might have thought that the question would be of interest in a discipline that presumes the superiority of markets and argues that how they work determines whether there can be full employment without inflation. You would have been wrong. I have scanned some of the key chapters from the book and made them available as html and pdf files from here.
Currently, I am working with a very bright, young, multi-disciplinary team modelling multi-agent interaction in complex institutional and technological environments. I am currently developing models to simulate strategic decision-making processes, model complex markets, extend such areas of soft management theory as soft systems methodology, the literature on core competencies and the whole area of the resource-based view of the organization to include more formal and computational elements.
Scott Moss and Bruce Edmonds (2005) “Towards Good Social Science”, Journal of Artificial Societies and Social Simulation vol. 8, no. 4 <http://jasss.soc.surrey.ac.uk/8/4/13.html>
Moss, S. and B. Edmonds (2005). Sociology and Simulation: Statistical and Qualitative Cross-Validation. Manchester, American Journal of Sociology 110(4): 1095-1131.
Moss, S. (2002). "Policy Analysis from First Principles." Proceedings of the US National Academy of Sciences 99(Suppl. 3): 7267-7274.
The gap in my publications record is due in large measure to having suffered a heart attack in February 2002. However, I have given a number of invited papers including to the 2005 Multi Agent Based Simulation workshop at AAMAS2005 and to a workshop in Koblenz, Germany on Epistemological Perspectives on Simulation.
Inaugural President, European Social Simulation Association (2002-2003)
Vice President for International Liaison, European Social Simulation Association (2003-present)
Steering Group, 2008 World Conference on Social Simulation, 2005-present
Executive group, European Complex Sciences Society, 2005-present
Coordinator, Agent Based Social Simulation special interest group of AgentLink, the Esprit Network of Excellence for Multi-Agent Systems
Programme chair of MABS2000, the 2nd Multi Agent Based Simulation workshop federated with ICMAS-2000
My leisure time was taken up largely by sailing the 10m steel cutter-rigged ocean-going boat Conachair which I shared with my wife Linda. We have taken her as far north as the Outer Hebrides and as far south as the Atlantic coast of Spain near the Portuguese border. I am a past Commodore of the Manchester Cruising Association. We recently sold the Conachair and hope soon to buy a replacement requiring less maintenance and perhaps requiring less brute strength as I move through my 60s.
CAVES: Complexity, Agents, Volatility,
Evidence and Scale
This project
is funded under the “Tackling Complexity” call of the New and
Emerging Science and Technology programme under European 6th
Framework. I am the project coordinator. The partners are The
Macaulay Institute in Aberdeen, Scotland, my longstanding
collaborator Tom Downing who heads the Oxford office of the
Stockholm Environmental Institute,
Andreas Ernst heading up the team at the Universität
Kassel, two Polish collaborators from the University of Wroclaw
(Andrzej Dunajski) and Politechnika
Wroclawska (Piotr Magnuszewski) and International
Institute for Applied Systems Analysis (Jan Sendzimir) in
Vienna. The purpose of the CAVES project is to couple policy
concerns for complex human-environmental systems with linked
physical, biological and social models. For more detail, click
here.
GIACS
This
is a coordinating action for the Tackling Complexity initiative
under the FP6 programme New and Emerging Science and Technology
(NEST). I am a member of the management committee.
NeWater
This
is one of the so-called Integrated Projects of the 6th
Framework. This means it is enormous with some 30 partners of one
sort or another and also, of course, anything but actually
integrated. I don't know too much about the project as a whole
except that it is about water management issues. My interest is in
demonstrating that some conventional techniques and concepts used by
other partners are at best unjustified and probably misleading. My
sights are set particularly on summary numerical measures of
vulnerability. Issues such as health, hunger, consequences of
flooding and drought are not usefully represented by numbers.
Performing arithmetic on such numbers to get a mean or any other
measure of central tendency must be sheer nonsense.
FIRMA: Freshwater Integrated Resource Management with Agents
This project arose from from a chance train journey in the company of Tom Downing, then of the Environmental Change Institute in the University of Oxford. We were returning from an ESRC funded workhop of UK modellers where I, as usual, trashed economic modelling and described the CPM alternatives. Tom, Claudia Pahl-Worstl, then of EAWAG (the Swiss federal research institute for water and climate change issues) and now at the University of Osnabruck, and I began thinking and contacting the best social simulation and integrated assessment modelling teams in Europe and the USA. The result was a successful application for Fifth Framework European funding (about 1.8 million euro) involving integrated assessment modelling teams from Oxford, EAWAG, Maastricht and Cemagref (France) as well as geographers in Barcelona and social simulation modellers from (in addition to the CPM), University of Surrey (Nigel Gilbert), Koblenz (Klaus Troitzch) and CNR-IP in Rome (Rosaria Conte). The project did not achieve all that I had wanted – in part perhaps because I didn't finish what I wanted to do as a result of a heart attack in February 2002. We have, however, had some good publications out of it including a paper in the American Journal of Sociology in January 2005. The project started on 1st March 2000 and ended on 28th February 2003.
IMIS: Intelligent Marketing Integrated System
IMIS was funded under the Intelligent Systems Integration
Programme ISIP
of the Engineering and Physical Sciences Research Council . Our
commercial collaborators were United Distillers and the Henley
Centre for Forecasting with (for a while) TSB before it merged with
Lloyds Bank.
The project funding finished in 1997. Although our research involved the market for alcoholic drinks, we have since been approached to apply the analysis in a range of fast moving consumer goods and in industrial markets sich as metals and plastics.
The project software uses KBS technology, genetic programming algorithms and non-linear regression methods to identify the competitive set for any one brand and then to identify why people buy the brands they do and the proportions of sales accounted for by each of those purposes. In the markets for alcoholic beverages, the determination of the competitive set of a brand is non-trivial. A brand of scotch whisky, for example, will compete with not only (some but by no means all) other brands of scotch whisky but also for some purposes brandies, bourbon, Canadian whiskey, Irish whiskey and even some curious liqueurs. The project software determines which of these possibilities are important and also how the competitive relations change over time. On the demand side of the market, the project software will take the brand attributes (eg, specialness, tradition, strength, uniqueness, and similar qualitative characteristics) as suggested by the marketing professionals and infer from price and volume sales data what sorts of demands there will be in the sense of desired degrees of each attribute, the importance of the attribute to the consumer and the consumer's tolerance to deviations from the ideal. Some demands are simply for the "physiological" effect (ie to get drunk) while other demands might be for social purposes (parties, weddings etc), self reward (after a promotion or other success), and so on. A detailed statement of our early algorithms for the determination of the demand side of these markets appeared in Omega (vol 25), 1997. For an earlier version of the paper, click here .
Moss, Scott (1998), "Critical Incident Management: An Empirically Derived Computational Model" Journal of Artificial Societies and Social Simulation 1, (4), <http://www.soc.surrey.ac.uk/JASSS/1/4/1.html>
Moss, Scott , Helen Gaylard, Steve Wallis and Bruce Edmonds (1998), SDML: A Multi-Agent Language for Organizational Modelling, Computational and MathematicalOrganization Theory 4, (1), 43-70.
Moss, Scott and Bruce Edmonds (1998), Modelling Economic Learning as Modelling, Cybernetics and Systems 29, (3), 215-247.
Moss, Scott and Bruce Edmonds, A Formal Preference-State Model with Qualitative Market Judgements, Omega - the international journal of management science, 25, (2), 1997.
Moss, Scott, Huw Dixon and Steven Wallis, Evaluating Competitive Strategies, International Journal of Intelligent Systems in Accounting, Fianance and Management, 4, (4), 1995, pp. 361-392..
Moss, Scott, Control Metaphors in the Modelling of Learning and Decision-Making Behaviour, Computational Economics 8, (4) 283-301, 1995.
Moss, Scott, Artis, M. and Ormerod, P., A Smart Macroeconomic Forecasting System, The Journal of Forecasting 13, (3) 299-312, 1994.
Moss, Scott, Winter's Fundamental Selection Theorem: A Disproof, The Quarterly Journal of Economics pp.1071-4, 1990.
Moss, Scott, Equilibrium, Evolution and Learning, The Journal of Economic Behaviour and Organisation, 1990.
Moss, Scott, Investment and Innovation over the Long Wave, Research Policy, pp. 211-8, 1986.
Moss, Scott, The Theory of the Firm from Marshall to Robinson and Chamberlin, Economica, 1984.
Moss, Scott, and Rae, J., eds. Artificial Intelligence and Economic Analysis: Prospects and Problems Cheltenham: Edward Elgar Publishing Ltd., 1993.
Moss, Scott, Markets and Macroeconomics Oxford: Basil Blackwell ,1984.
Moss, Scott, An Economic Theory of Business Strategy Oxford: Basil Blackwell,1980.
Tel: +44 61 247 3886 Fax: +44 61 247 6802
Centre
for Policy Modelling, Manchester Metropolitan University, Aytoun
Building, Aytoun Street, Manchester M1 3GH, United Kingdom.
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