Just to let folks on this list know, we have a group at Statistics Canada
that has been developing microsimulation models for over a decade  Until
recently, these have all been real, "industrial strength " dynamic,
agent-based models in the areas of pension policy, student loan policy, and
health policy (e.g. see "POHEM -- A Framework for Understanding and Modeling
the Health of Human Populations", World Health Statistic Quarterly, World
Health Organization, Geneva, Vol 47 (3/4) 1994).

 In order to improve the efficiency of our development process, we created a
few years ago a modeling language, ModGen.  This is a C++ pre-compiler that
cuts our code writing about 80% for new models and component modules.

I've recently used ModGen to write our first "toy" model, a theoretical
model, XEcon (an "experimental economy" model in the spirit of Nelson and
Winter, Lane, Dosi, etc.), which is briefly described in Conte, Hegselmann
and Terna's Springer 1997 book (longer version forthcoming in the Canadian
Journal of Economics).  (It is very much motivated by the kinds of
frustrations with mainstream economic theory that I see in a number of
postings to this list.)

We are also using ModGen for a model called LifePaths, which is an attempt
at a new kind of integrated framework for socio-economic statistics (and
indicators) with explicit micro-foundations and a life course perspective.
A copy of the most recent version is available at  "'Good Life Time' (GLT):
Health, Income and the Time to Enjoy Them"  (787Kb).  An earlier version was
also briefly described in Rosaria's book.

ModGen is also being used by Canada's Human Resources Development Ministry
for the new version of their occupational projection model.

I have been watching this discussion for some time, and hoping to get around
to posting something about ModGen.  This most recent discussion of
simulation language spec's has tipped the balance.  Please treat this as a
kind of pre-commitment to provide some URL's where interested folks can find
out more about ModGen.

Cheers -- Michael Wolfson


----- Original Message -----
From: Scott Moss <[log in to unmask]>
To: simsoc <[log in to unmask]>
Sent: March 9, 1999 8:07 AM
Subject: MAS methdology: a proposal


>A little while ago on this list, I asked whether anyone knew of any
>agent specifications as formalisms -- logics, cellular automata or other
>mathematical representations -- that had ever been implemented in a
>problem space or to perform some function on a useful scale.  Only one
>suggestion was offered: Rafael Bordini suggested the implementation of
>an air traffic control system in Australia using a programming
>architecture (PRS which became dMARS and then, I believe, AgentSpeak).
>PRS and dMARS were developed by Rao and Georgeff then at the Australian
>AI Institute (AAII).  In looking at that literature, I have the
>impression that both PRS and now dMARS use the semantics of BDI logics.
>It does not seem as if applications developed in these architectures are
>necessarily sound and consistent relative to the BDI logic reported by
>Rao and Georgeff in, for example, their ICMAS '95 paper.  Indeed, I have
>not found any such claim by anyone working on agent architectures using
>BDI semantics.  To offer two examples:
>
>Martin, Cheyer and Moran at the Artificial Intelligence Center of SRI
>International wrote in October '98:
>
>     Another influential approach, which makes stronger assumptions
>     about the knowledge and processing used within individual
>     agents, is based on the structuring of agents' activities
>     around the concepts of Belief, Desire and Intention (BDI) [Rao
>     and Georgeff1995].  While BDI's emphasis on a higher level of
>     abstraction has been extremely important in giving direction
>     to work on agent based systems, its applicability may be
>     limited by the structural requirements imposed on individual
>     agents, and by difficulties in interoperating with legacy
>     systems.
>
>In fact, if we go back to the Rao-Georgeff paper, they seem to have kept
>the theoretical and implementation perspectives quite distinct in
>reporting their air traffic management application. To say that they are
>related is not to say that they are identical.
>
>There is no doubt that the work at AAII has shown the BDI semantics to
>be a useful and insightful approach to the development of large-scale,
>real-time systems.  What is in doubt is whether those systems are
>implementations of the BDI formalism alone and, to the extent that other
>elements enter into those systems, whether their success is due to the
>BDI formalism or to the other elements of the programming environment or
>some aspect of the combination.
>
>I presume that the interest of both the computer science and social
>simulation communities in multi-agent systems is in the systems rather
>than the agents alone.  That is to say, the agents are a programming
>device in computer science intended as components of reliable systems
>with predictable behaviour and known applicability. Social scientists
>are interested in the interactions among individuals and how these give
>rise to and are mediated by institutional arrangements. Moreover, the
>virtue of the formalisms is that they enable us to specify properties of
>the phenomena they are used to represent.  It would seem, therefore, to
>make some sense to ensure that our representations of systems are
>consistent and sound relative to an appropriate formalism and that our
>agents are specified pragmatically.
>
>There is, however, a further reason for taking a systems perspective
>here.  The successful sciences -- paradigmatically, physics, chemistry
>and biology -- develop by explaining and predicting observations.
>Special relativity theory arose from the need by physicists to bring
>together the Galilean principle of relativity which conformed both to
>common observation and to such fundamental laws of physics as the law of
>inertia with the (then) more recent observational and theoretical truth
>that the speed of light is constant and independent of the relative
>velocity and direction of motion of the light source.  I don't know much
>(i.e. anything) about particle physics but I do read that observation,
>experimentation and the consequences for theory lead physicists to
>postulate new concepts of particles ***together with the evidence that
>the existence of such particles would manifest***.  The double helix was
>identified on the basis of x-ray crystallographic observation and also
>offered a physical explanation for geneticists' observations and,
>ultimately, Darwinian evolution.  Important elements in chemistry
>emerged in large measure from observation and production requirements in
>the French bleaching industry.
>
>In the social sciences, business historians (Chandler and Penrose) and
>historians of technology (Rosenberg and David) have documented the ways
>in which the need to solve problems gives rise to analytical,
>organizational and technological developments that not only solve the
>problems they were developed to address but enhance the  capacities of
>scientists, managers, technologists, et al to engage in new activities
>and develop new science and technology.
>
>In all of these cases, successful developments follow from starting with
>the problem and developing the techniques to resolve those problems. The
>solution techniques also provide new opportunities that themselves
>frequently require other, related problems to be solved resulting in yet
>further capacities for activity or understanding.
>
>Consider, for a moment, disciplines that do not include in their
>theoretical research agenda the solution of empirical problems.  My own
>favourite example, of course, is economics.  The commitment of
>economists to the representation of agents as utility-maximizers is
>bound up with the focus of analysis on equilibrium.  The inventors of
>the economic equilibrium concept (e.g. John Bates Clark, 1893 and Alfred
>Marshall, 1895) put it forward as a transitional step to a fully dynamic
>analysis of economic systems.  Since no such system has every been
>observed and, we now know, that unlimited computational capacity is a
>necessary condition for equilibrium (Radner, 1968) in dynamic economies
>where not everyone is identical, it has not been an objective of
>economic theory to represent anything we actually observe.  What is true
>is that the semantics of equilibrium theory are frequently used to
>justify policy prescriptions intended to influence real socio-economic
>systems.  Without the link between observation and problem solving on
>the one hand and the development of agent representations on the other
>hand, the creative tension that has driven the successful sciences,
>technology and the development of social institutions has been absent.
>Even economists who consider themselves heterodox, assert some features
>of economies to be more plausible than those assumed by conventional
>economists, then invent out of their own heads some representation of
>agent cognition and then define an environment that the invented agent
>representation can act on.  There is still no demonstrable link between
>such a model and the world we observe.
>
>The answers, or lack thereof, to my original question about the
>existence of practical applications of agents represented as formalisms
>supports my conjecture that a successful research programme is unlikely
>to start with such formalisms as agents in the hope that eventually
>useful social science will emerge.  By useful, I mean social science
>that explains a wide range of observations in an integrated manner and,
>as a result, supports model based policy analysis. A successful social
>science seems, on the historical record, more likely to emerge from a
>usefully scaled, problem oriented approach than from an approach in
>which the problem space is driven by agent representations.
>
>The proposal is a programme for the social simulation community in which
>we identify some key social problems to address or observations to
>explain and model the systems or represent the relevant environment in a
>manner that will capture the problem area or observation independently
>of any agent specification.  Agent based means should be developed to
>assess the confidence we can have in our model based analyses.  That is,
>I propose that we identify on the basis of domain expertise who are the
>important real actors and then develop agent representations of those
>actors that are as detailed and descriptively accurate as our
>computational resources allow.  This connection between observation and
>agent representation will constitute the conditions of application of
>the models.
>
>Clearly, it is not possible to use such fine-grained presentations at
>the useful scale required.  Techniques for abstracting from the
>entailed representations of agents are also required.  Such abstractions
>should be specified in such a way that we can ascertain by statistical
>and qualitative means that the abstractions do not entail agent
>behaviours that distort the behaviour of the more fine grained agent
>representations.  This in itself seems a fertile field for exploration.
>There might well be cascades of such models so that the finest grained
>representations are abstracted for use in (say) models of a region and
>these in turn are abstracted to model interactions among regions and so
>on up to a global scale.  There might also turn out to be some useful
>canonical forms of models such as suggested in
><http://www.cpm.mmu.ac.uk/cpmrep49.html> that could be used in a variety
>of applications.
>
>There are some agent representations already in the literature that
>attempt to capture real agents, their behaviour in social systems and
>the interactions between the behaviour of those individuals and relevant
>representations of their social systems. Examples include Rouchier's
>model of potlatch reported at MABS98 or my own model of critical
>incident management in JASSS.  But these are both free-standing models
>that have not been used to support models of larger or higher level
>systems.
>
>To support aggregation and scaling up in general, the most natural set
>of abstract representations to start with will be those we already know:
>deontic and bdi logics, finite cellular automata, simulated annealing ,
>genetic programming, and so on.
>
>It will be clear from the above that I am not suggesting a problem
>oriented, model based, usefully scaled social science as an alternative
>to the representation of agents by logical and mathematical formalisms.
>What I am suggesting is that such formalisms do not easily connect with
>observation and social problems in any direct way either in their
>specification or in the problem spaces they support.  As such, they
>might well provide links between agent representations that are as far
>as possible descriptively accurate and scaled up problem and policy
>analysis.
>
>I turn finally to the properties of the systems within which the agents
>are represented.
>
>For purposes of policy analysis and generally for understanding social
>systems, it will always be useful to identify any properties of those
>systems that are or are not sensitive to particular patterns or
>representations of individual behaviour.  My argument against building
>policy oriented models entirely on agents represented by formalisms was
>entirely pragmatic and based on observation: such formalisms do not in
>practice provide conditions of application of the models and they have
>not led to usefully scaled models.  There is a cost here in that the
>absence of a formal basis for the agent representation allows
>ambiguities and contradictions to survive unnoticed.
>
>The methodology proposed here would yield models at a useful scale for
>policy analysis with sufficient abstraction from detail to meet the
>limits of computational capacity and the cognitive capacities of policy
>analysts. If such models were, in addition, known to be sound and
>consistent relative to some logical formalism, the virtues of the
>avoidance of ambiguity and self-contradiction could be had without the
>costs associated with the use of such formalisms as the lowest level
>agent representations.
>
>This seems to me to be a virtue of strictly declarative languages such
>as SDML.  Because SDML conforms to a known formal logic (a fragment of
>strongly grounded autoepistemic logics -- FOSGAL), any model that runs
>in SDML is consistent and sound relative to FOSGAL.  Different logical
>formalisms can still be used to represent individual agents of any
>grain.  But the representation of the system itself has the properties
>of a FOSGAL formalism with the attendant possibilities (in principle
>though typically difficult in practice) for proving theorems about
>relationships identified in simulation experiments.
>
>
>
>--
>Scott Moss
>Director
>Centre for Policy Modelling
>Manchester Metropolitan University
>Aytoun Building
>Manchester M1 3GH
>UNITED KINGDOM
>
>telephone: +44 (0)161 247 3886
>fax: +44 (0)161 247 6802
>
>http://www.cpm.mmu.ac.uk/~scott
>