🚀 go-pugleaf

Volume: 30, Issue: 6, Thu Dec 17 16:19:53 EST 1992

+----------------+
| TODAY'S TOPICS |
+----------------+

 
[NEW QUESTIONS]
  Books on Simulation
  Hierarchical Decomposition
  Introductory Books
  Synchronization in Distributed Systems
  VHDL vs. Verilog
  Boat Simulator
  ModSim Compiler
[SOFTWARE]
  Simulating the US Budget
  LOOPN: Object Oriented Petri Net Simulator
[CALL FOR PAPERS/PARTICIPATION]
  Annual Simulation Symposium
  Summer Computer Simulation Conference (SCSC '93)
[DEPARTMENTS]
  Simulation in the Service of Society

* Moderator: Paul Fishwick, Univ. of Florida
* Send topical mail to: simulation@bikini.cis.ufl.edu OR
  post to comp.simulation via USENET
* Archives available via FTP to bikini.cis.ufl.edu (128.227.224.1).
  Login as 'anonymous', use your e-mail address as the password, change
  directory to pub/simdigest. Do 'binary' before any file transfers.
* Simulation Tools available by doing above and changing the
  directory to pub/simdigest/tools. 

 


-----------------------------------------------------------------------------


Subject: [NEW QUESTIONS]


------------------------------


Date: Wed, 9 Dec 1992 20:06:19 GMT
From: news@ns1.nodak.edu (News login)
To: comp-simulation@uunet.UU.NET
Content-Length: 1409
X-Lines: 32


Newsgroups: comp.simulation
Path: plains.NoDak.edu!bhati
From: bhati@plains.NoDak.edu (Amit Bhati)
Subject: req., suggestions on text books in Simulation
Sender: usenet@ns1.nodak.edu (News login)
Date: Wed, 9 Dec 1992 20:06:18 GMT
Nntp-Posting-Host: plains.nodak.edu
Organization: North Dakota Higher Education Computing Network

Hi folks, I am a starting Grad. student in CS and want to learn the theory,
programming (for my present work it will be in C/C++) and all the Statistics
necessary in basic Computer based Simulation. However, I am fairly ignorent
of the area and would like people to suggest a good, comprehensive book(s)
to read on introductory to intermediate stuff in Simuation. You may suggest
advanced stuff, that I could use later too.
   If it may help, my work shall be in investigating Scheduling strategies
for multi-processor systems.
   I do not read this news-group regularly, so replies by e-mail shall be
greatly appreciated. Many thanks in advance.

Cheers,
Amit
 -- 

=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|Amit Bhati		|bhati@plains.NoDak.edu| "All that is there in the|
|Computer Science Dept.,|!uunet!plains!bhati   |  middle of the road is a |
|IACC Building, NDSU,   |Ph: (701) 232-6754    |  dumb, yellow line and a |
|Fargo, ND 58105.	|                      |  dead skunk."            |



------------------------------

Date: Fri, 11 Dec 92 17:08 GMT
From: Evan Gamblin <0001847804@mcimail.com>
To: "simulation@uflorida" <simulation@bikini.cis.ufl.edu>
Subject: Hierarchical.decomposition software
Content-Length: 1210
X-Lines: 32

In the early 1960's the architect Christopher Alexander used graphical 
decomposition as a means of understanding and solving complex community
planning problems having hundreds of potentially conflicting variables.


To do this, Alexander and an associate at MIT Marvin Manheim, wrote 
several programs for the IBM machine at their disposal. Does anyone know
whether these (or progeny) have been ported to the Mac or IBM-PC, and where 
I'd find them? (I have newsgroup and mail/mail server access only).
 
Otherwise, what tools can you recommend for this task?
 
The programs Alexander wrote were HIDEC2: a computer program for the 
hierarchical decomposition of a set with an associated graph (MIT Civil 
Engineering Sys Lab Publication no. 160, 1962) and HIDECS3: Four computer
programs for the hierarchical decomposition of systems which have an 
associated linear graph (MIT Civil Engineering Sys Lab research report 
no R63-27, 1963).
 
Appreciate any assistance you can p
rovide,
 
Evan Gamblin
 
The Halifax Group
Ottawa, Ont Canada
 
E-mail: 0001847804@mcimail.com



------------------------------

Date: Sun, 13 Dec 1992 22:29:37 GMT
From: news@ns1.NoDak.edu (News login)
To: comp-simulation@uunet.UU.NET

Hi folks, I am a starting Grad. student in CS and want to learn the theory,
programming (for my present work it will be in C/C++) and all the Statistics
necessary in basic Computer based Simulation. However, I am fairly ignorent
of the area and would like people to suggest a good, comprehensive book(s)
to read on introductory to intermediate stuff in Simuation. Specially a book
that will cover the required Statistics very well will be highly appreciated.
You may suggest advanced stuff, that I could use later too.
   If it may help, my work shall be in investigating Scheduling strategies
for multi-processor systems.
   I do not read this news-group regularly, so replies by e-mail shall be
greatly appreciated. Many thanks in advance.

Cheers,
Amit
 -- 

=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=
|Amit Bhati		|bhati@plains.NoDak.edu| "All that is there in the|
|Computer Science Dept.,|!uunet!plains!bhati   |  middle of the road is a |
|IACC Building, NDSU,   |Ph: (701) 232-6754    |  dumb, yellow line and a |
|Fargo, ND 58105.	|                      |  dead skunk."            |



------------------------------

To: comp-simulation@ucsd.edu
Path: delilah!johansen
From: johansen@delilah.ccsf.caltech.edu (Jonathan Johansen)
Newsgroups: comp.simulation,comp.parallel,comp.realtime,sci.engr.control
Subject: Synch of Distributed Clocks
Date: 13 Dec 1992 22:34:31 GMT
Organization: California Institute of Technology, Pasadena
Distribution: world
Nntp-Posting-Host: delilah.ccsf.caltech.edu

 
I'm developing a distributed simulation which employs two kinds
of processes.  I'll refer to these processes as the sensor and 
tracker processes.  The sensor processes simulate noisy 
measurements of a physical phenomena.  As a sensor generates each
discrete data set, it broadcasts the data to each each of the
tracker processes over a workstation LAN.  The communication is
is supported via UNIX sockets.  After receiving data blocks
from all sensors, the trackers try to fuse this multiple sensor
data into a noise free estimate of the physical phenomena's state.
 
I would like to have the sensors broadcast information based 
upon a realtime clock.  That is, each time a sensor process
sends a data messsage, it would time-stamp the message with
the local "wall clock" time of its computing node.  This method
is in contrast to a system which uses a simulated clock's time
stamp; i.e., a loop of the sort:
   sim_time = 0.0;
   while (!end_simulation) {
     generate_sensor_data();
     send_data_2_trackers();
     sim_time = sim_time + dt;
     }
I want to use a simulation time based upon the UNIX gettimeofday()
function.  The problem is that in a distributed environment, the
"wall clock" time of the multiple sensor processes may be slightly
different.  It's important that the tracker nodes receive data
blocks which are stamped with a time that accurately reflects
when the data was generated.  Is anybody aware of work that has
been done on synchronizing the "wall clocks" of distributed 
computing nodes?  For example, synchronizing the clocks of multiple
workstations on an Ethernet LAN.  Preferably to at least a 
10 millisecond accuracy.  Please respond with email, and I will
summarize with a posting.  Thanks.
 
Jonathan Johansen
Engineering Physics Division
Argonne National Laboratory
johansen@delco.ep.anl.gov



------------------------------

To: comp-simulation@ames.arc.nasa.gov
Path: juts!oran
From: oran@spg.amdahl.com (Oran Davis)
Newsgroups: comp.lang.vhdl,comp.lang.verilog,comp.simulation
Subject: VHDL vs Verilog the eternal question, collecting input.
Date: 15 Dec 92 18:19:24 GMT
References: <1992Dec15.004358.22535@EE.Stanford.EDU>
Organization: Amdahl Corp., Sunnyvale CA


Hi Netters,

We are users of both Verilog and VHDL under synopsys
all of us have our favorite system
that we will not give up. I am collecting a list of pros and cons to 
summarize the strengths and weaknesses of these two contenders.

Any ideas input of strong feeling are welcomed. Please keep in mind that
the project I am targeting for is VERY large with MANY people involved.

Thanks for any input,

	>- Oran



------------------------------

To: comp-simulation@uunet.UU.NET
From: jhsegal@wiscon.weizmann.ac.il (Livian)
Newsgroups: comp.simulation
Subject: Boat Simulator
Date: 15 Dec 92 11:41:47 GMT
Sender: news@wisipc.weizmann.ac.il
Organization: Weizmann Institute of Science, Computation Center


I don't know how many people read this newsgroup out there,but if anyone
reads,I would like to receive a recomandation of a war ship/boat/vessel.
Thank you very much.
                                   ___ 
                                  (o o)                  
 ------------------------------ooO-(_)-Ooo------------------------------------ 
  __     __  __    ___ __     _     ___   __                                  
    |      |   \     /   |     \       \    | VM/CMS:                          
    |      |    \   /    |    _ \       \   | JHSEGALL @ Weizmann.weizmann.ac.il
    |___   |       /     |    _  \    |  \  | UNIX:                             
        |  |      /      |   /    \   |     |  JHSEGAL  @ Wiscon.weizmann.ac.il   



------------------------------

Date: Wed, 16 Dec 92 23:58:48 -0600
From: David A Dreyer <etcdad@iitmax.iit.edu>
To: comp-simulation@rutgers.edu

Hi all....

I'm looking for a ModSim compiler, preferably for the 
PC, although any machine or environment will do.

Does anybody know of one in ftp-land or shareware land?  Any
info on the subject is mucho appreciated.

Thanks,

Dave




------------------------------


Subject: [SOFTWARE]


------------------------------


Date: Sat, 12 Dec 92 05:49:37 -0600
From: gmk@pegasos.ccsr.uiuc.edu (Gottfried Mayer-Kress)
To: mcleod@sdsc.edu
Subject: Budget Balancer ?

I read about a very nifty simulation program that should allow
you to test different strategies to balance the US budget. It is
called "Budget Balancer" and distributed by Banner Blue
Software. 


 ---
Gottfried Mayer-Kress
Center for Complex Systems Research, Department of Physics
3025 Beckman Institute, 405 N Mathews, Urbana, Il 61801
gmk@pegasos.ccsr.uiuc.edu (NeXT-Mail)
gmk@goshawk.lanl.gov, gmk@santafe.edu 

(217)-244-5877 (voice/fax modem),x8371(fax), x1994 (msg)



------------------------------

Newsgroups: comp.simulation
Path: probitas!charles
From: charles@probitas.cs.utas.edu.au (Charles Lakos)
Subject: LOOPN - Language for Object-Oriented Petri Nets
Sender: news@newsroom.utas.edu.au
Organization: University of Tasmania, Australia.
Date: Tue, 15 Dec 1992 05:22:20 GMT
Apparently-To: simulation@bikini.cis.ufl.edu


I wish to announce the availability of a compiler, simulator and associated
source control for an object-oriented petri net language called LOOPN.

In LOOPN, a petri net is an extension of coloured timed petri nets.  The 
extension means firstly that token types are classes.  In other words, they 
consist of both data fields and functions, they can be declared by 
inheriting from other token types, and they can be used polymorphically.  
The object-oriented extensions also mean that module or subnet types are 
classes.  In other words, the declaration of a subnet encapsulates 
constants, types, places, transitions, access functions and parameters.  
These module types may be declared by inheriting from other modules and can 
be used polymorphically.

LOOPN has been developed over a period of about 5 years at the University of
Tasmania, where it has been used in teaching computer simulation and the
modelling of network protocols.

Further details on the syntax of LOOPN, its application and the conditions
under which it is distributed are available in the distribution.

The LOOPN compiler, simulator and associated documentation is available by
anonymous ftp from 
        ftp.utas.edu.au         (131.217.10.1)
as 
        departments/computer_science/loopn.tar.Z

 -- 
 --
Charles Lakos.                               C.A.Lakos@cs.utas.edu.au
Computer Science Department,                 charles@probitas.cs.utas.edu.au
University of Tasmania,                      Phone:    +61 02 20 2959
Sandy Bay, TAS, Australia.                   Fax:      +61 02 20 2913



------------------------------


Subject: [CALL FOR PAPERS/PARTICIPATION]


------------------------------


Date: Thu, 10 Dec 92 17:18:11 EST
From: jam@pollux.cs.uga.edu (John Miller)
To: simulation@bikini.cis.ufl.edu
Content-Length: 11145
X-Lines: 300

                   26TH ANNUAL SIMULATION SYMPOSIUM

                       in conjunction with the
                   1993 Simulation MultiConference

             March 29 - April 1, 1993  *  Washington D.C.

This year's Simulation Symposium will be presenting 37 state-of-the-art
papers from industry, government, and academia.  These 37 papers were
selected from 50 full paper submissions which were fully refereed by a
25 member Program Committee.  Papers will be presented in the following
topical areas:  Parallel Simulation, Neural Networks, AI & Simulation,
Object-Oriented Simulation, Simulation Languages & Environments,
Simulation of Parallel Processors, and Simulation of Circuits, Networks,
Distributed Systems and Databases.

The Annual Simulation Symposium is the oldest continuously operating
conference/symposium dedicated to simulation.  The Symposium features
the Ira M. Kay Memorial Best Paper Award of $500.  Also, authors of
top papers will be encouraged to submit a follow-on paper to the
International Journal in Computer Simulation.

The Symposium is sponsored by the Society for Computer Simulation (SCS)
in cooperation with the IEEE Computer Society and ACM/SIGSIM.

The 26th Annual Simulation Symposium will be held at the

                        Key Bridge Marriott
                          1401 Lee Highway
                    Arlington, Virginia  22209
                      phone:  (703) 524-6400
                      fax:    (703) 243-3280

The hotel is just across the bridge from Georgetown in Washington D.C. 
It is only two blocks from the Metro (subway) and downtown Washington
is just minutes away.  For additional information please contact the
Symposium President/General Chair:

                           John A. Miller
                     Computer Science Department
                        415 Graduate Studies
                        University of Georgia
                          Athens, GA  30602
                        phone: (706) 542-3440
                     email: jam@pollux.cs.uga.edu

For information on next year's Symposium (the 27th) please contact
Patrick W. Dowd; email: dowd@eng.buffalo.edu.

________________________________ cut _________________________________
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______________________________________________________________________


                      Preliminary Program

                26th Annual Simulation Symposium


General Chair: John A. Miller, University of Georgia
Program Chair: Philip A. Wilsey, University of Cincinnati


SESSION 1:  Parallel and Distributed Simulation
    Monday, March 29, 10:30-12:00 a.m.
    Chair: George W. Zobrist

    Evaluation of Three Approaches to Parallel Logic Simulation
     on a Distributed Memory Multiprocessor
    by Peter Luksch, Technical University of Munich, Germany

    Reducing Rollback Overhead in Time-Warp Based Distributed
     Simulation with Optimized Incremental State Saving
    by Herbert Bauer and Christian Sporrer,
     Technical University of Munich, Germany

    Performance Measures for Several Optimizations to a
     Distributed Digital System Simulator
    by Avinash C. Palaniswamy, Sandeep Aji, Philip A. Wilsey,
     University of Cincinnati

    WarpLog: Time Warped Logical Objects
     by Krys J. Kochut and John A. Miller, University of Georgia


SESSION 2:  Parallel Architectures I
    Monday, March 29, 1:30-3:00 p.m.
    Chair: Enrique V. Kortright

    The CacheMire Testbench -- A Flexible and Effective Approach
     for Simulation of Multiprocessors
    by Mats Brorsson, Fredrik Dahlgren, Hakan Nilsson and
     Per Stenstrom, Lund University, Sweden

    Implementation and Simulation of Acquisition Consistency
     for Cache-Based Multiprocessors
    by Yung-Syau Chen, University of Southern California

    Multilevel Simulation of Distributed-Memory Program Traces
    by Jean-Pierre Prost and Shlomo Kipnis,
     IBM T.J. Watson Research Center

    Latency Hiding Strategies for Pre-Allocation Based
     Media Access Protocols for WDM Photonic Networks
    by Krishna M. Sivalingam and Patrick W. Dowd,
     State University of New York at Buffalo


SESSION 3:  Parallel Architectures II
    Monday, March 29, 3:30-5:00 p.m.
    Chair: Herbert Bauer

    Throughput Analysis of Multiple-Bus Multiprocessor Systems
     with Simultaneous Possession of Common Resources
    by Ibrahim Onyuksel, Northern Illinois University

    Performance Evaluations of Adaptive Wormhole Routing
     with Three Different Selection Functions
    by Ziqiang Liu and Handong Wu,
     Royal Institute of Technology, Sweden

    Interaction of Cache Coherency and Media Access Protocols in
     the Optically Interconnected Distributed Memory Environment
    by John Chu, Kwan AuYeung and Patrick W. Dowd,
     State University of New York at Buffalo

    A Parallel Algorithm for Dynamics Simulation of Multibody Chains --
      Implementation on a Transputer System
    by Burke Pond and Inna Sharf, University of Victoria, Canada


SESSION 4:  Networks and Distributed Systems
    Tuesday, March 30, 8:30-10:00 a.m.
    Chair: Yi-Bing (Jason) Lin

    The Limited Benefits of Load Sharing in Multicomputer Systems
    by Sayed A. Banawan, University of Houston

    Design and Analysis of a Network Level Channel Abstraction
     to Support Real-Time Communications
    by Taieb Znati and Brian Field, University of Pittsburgh

    The Simulation of Coadaptive Decision-Making
    by Alexander Glockner, Bowie State University

    Maximizing the Benefit of Load Information
    by Margaret Schaar, Louisiana Tech University


SESSION 5:  Distributed Systems and Databases
    Tuesday, March 30, 10:30-12:00 a.m.
    Chair: Krys J. Kochut

    A Database Simulation Environment and Its Use for Evaluation
     of Replication Control Algorithms
    by Sang H. Son and Spiros Kouloumbis, University of Virginia

    Simulating the Global Directory Service with OPNET
    by Alex Andrianopoulos and David Chadwick,
     University of Salford, United Kingdom

    The Use of VHDL as the Database for the Complete Electronic
     Hardware Design Cycle
    by William A. Hanna, McDonnell Douglas Electronics

    Simulation of Optimized Distributed Query Schedules on a
     Hypercube Network
    by S. Popovich, M. Alam and S. Bandyopadhyay,
     University of Windsor, Canada


SESSION 6:  Object-Oriented Simulation: Tools and Languages
    Tuesday, March 30, 1:30-3:00 p.m.
    Chair: Avinash Palaniswamy

    CEDES - A C++ Engine for Discrete Event Simulation
    by Jhyfang Hu, Tulane University

    A C++ Environment for Modeling Communication Systems
    by Wlodek Dobosiewicz and Pawel Gburzynski,
     University of Alberta, Canada

    COPS: A Computer Operations Performance Simulation System
    by Dennis Mok, Dan Daly, Joe Geigel, Krishna Kant, 
     Yi-Bing (Jason) Lin and Victor Mak, Bellcore

    Simulation of the Object Flow Model: A Conceptual Modeling
     Language for Object-Driven Applications
    by Jyotsna Naranswamy, Lois Delcambre and Lissa Pollacia,
     University of Southwestern Louisiana


SESSION 7:  Object-Oriented Simulation: Environments
    Tuesday, March 30, 3:30-5:00 p.m.
    Chair: Patrick W. Dowd

    An Integrated Modeling Environment for Object-Oriented Simulation
     of Ecological Models
    by Luca Del Furia and Andrea Rizzoli, Politecnico di Milano, Italy

    A Multi-Domain Tool for Building Object-Oriented Animation
     Environments of Simulation Results
    by M. Gourgand, D. Hill and P. Kellert,
     Universite Blaise Pascal - Clermont-Ferrand II, France

    Towards an Object-Oriented Simulation Environment for
     Manufacturing Systems Analysis
    by Michel Fabre and Daniel Leblanc,
     Ecole Polytechnique de Montreal, Canada


SESSION 8:  Object-Oriented Simulation: Applications
    Wednesday, March 31, 8:30-10:00 a.m.
    Chair: Lissa Pollacia

    Object-Oriented Simulation of Capability Based Architectures
    by Stephen R. Daily and Mansur H. Samadzadeh,
     Oklahoma State University

    Building Hierarchical Symbolic Models and Large Scale Simulations
     Using the General Simulation System
    by William C. Cave and Naeem Malik,
     PSI Simulation Systems Corporation

    Object-Oriented Simulation for Air Traffic Control Training
    by Andrew J. Kornecki, Embry-Riddle Aeronautical University


SESSION 9:  Scheduling Algorithms and Operating Systems
    Wednesday, March 31, 10:30-12:00 a.m.
    Chair: Margaret Schaar

    Computational Schemes for Efficient Simulation of Service
     Disciplines
    by Janche Sang, Ke-Hsiung Chung and Vernon Rego, Purdue University

    Effects of Nonsymmetric Release Times on Rate Monotonic Scheduling
    by Randall G. Karl, T. Leo Lo and Daniel C. St.Clair,
     University of Missouri-Rolla

    A Practical Approach to Using Simulation to Analyze the Effects
     of a Non-Poisson Stream on Traffic in a Switch
    by Steven C. Adamson, Steven P. Gordon and Donald G. Otterbein,
     Booz, Allen & Hamilton, Inc.

    Dynamic Performance Profiles of Simulation Calendars
    by Ke-Hsiung Chung, Janche Sang and Vernon Rego, Purdue University


SESSION 10:  AI & Simulation
    Wednesday, March 31, 1:30-3:00 p.m.
    Chair: Mansur H. Samadzadeh

    Development of Intelligent Scheduling Aids Using Simulation and
     Neural Networks
    by Yuehwern Yih, Luis C. Rabelo, Albert T. Jones and
     George M. Witzgall, Purdue University

    Using the PARLOG Parallel Programming Language for the Performance
     Analysis of Concurrent Systems
    by Vincenza Carchiolo and Maurizio Papale,
     Istituto di Informatica e Telecomunicazioni, Italy

    A Stochastic Simulation Model of Family Income
    by Thomas Wong and David Tepper,
     Baruch College, City University of New York


SESSION 11:  26th Annual Simulation Symposium Business Meeting
    Wednesday, March 31, 3:30-5:00 p.m.

______________________________________________________________________
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________________________________ cut _________________________________




------------------------------

From: tttron@escher.lerc.nasa.gov (William Krauss)
Subject: Call for Papers...
Sender: news@eagle.lerc.nasa.gov
Nntp-Posting-Host: escher.lerc.nasa.gov
Organization: NASA Lewis Research Center [Cleveland, Ohio]
Date: Wed, 16 Dec 1992 20:22:45 GMT
Apparently-To: comp-simulation@cis.ohio-state.edu




 		             Call For Papers

	      Summer Computer Simulation Conference (SCSC `93) 
		           Boston, Massachusetts
		             July 19-21, 1993  

	     (Sponsored by The Society for Computer Simulation)



                Computer Graphics Techniques for Simulations

"SCSC `93 will feature presentations of innovative work, state-of-the-art 
technology reviews, panel discussions, tutorials, and trade exhibits designed 
to provide comprehensive coverage of this rapidly evolving field.  Major 
themes will be emerging and enabling technologies, simulation methodologies 
and practices, industrial applications, validation and verification techniques,
and the cost benefits of rapid prototyping."

One very important enabling technology for simulation is computer graphics
(visualization techniques, animation, etc.).  Visual feedback of simulated 
systems can provide rapid, intuitive, and meaningful insight to researchers
and engineers especially during prototype phases.  

Simulation and computer graphics methods have recently become "married" into 
a "single" integrated technology.  The field of virtual environments, an ideal 
example of this relationship, relies heavily upon the recent advancements 
in both of the respective disciplines. 
  
I am organizing a session within the Simulation Methodologies Group and 
am searching for speakers who are interested in presenting work in the 
aforementioned areas. 

Abstracts of approximately 500 words should be submitted by anyone 
interested.  

Please contact me via e-mail, US mail, or phone.



 -- 
>>>> W.D. Krauss  	                     Sverdrup Technology, Inc.  <<<<
>>>> NASA Lewis Research Center   MS 142-5    Cleveland, OH 44135  USA  <<<< 
>>>> william@lerc.nasa.gov                              (216) 433-8991	<<<<



------------------------------


Subject: [DEPARTMENTS]


------------------------------


Date:    Wed, 16 Dec 92 11:04:15 GMT
From: mcleod@Sdsc.Edu
Subject: E-S3-1-11-Part One
 
The following sample issue of our electronic magazine, "E-S3",
covering selected topics about computer modeling and simulation,
is sent to you with the compliments of the publisher of the
technical journal SIMULATION, the Society for Computer
Simulation, and John and Suzette McLeod, the Editors of
Simulation in the Service of Society (S3), a special section of
that journal.  If you do not care to receive future issues please
type REPLY -- cancel.  Let's not clutter up our E-mail with "Junk
Mail"!
__________________________________________________________________
 
                  E-S3  Vol. 1, No. 11, Part One
 
              based on the November 1992 issue of
             "Simulation in the Service of Society"
John McLeod, Technical Editor     Suzette McLeod, Managing Editor
          8484 La Jolla Shores Dr., La Jolla, CA 92037.
                   E-mail: mcleod@sdsc.bitnet
                                *
                   S3 is a special section of
                           SIMULATION
                   the monthly journal of the
                 SOCIETY for COMPUTER SIMULATION
             P.O.Box 17900, San Diego, CA 92177-7900
            Phone: (619) 277-3888 FAX: (619) 277-3930
                                *
[Copyright  Notice:  E-S3 is the electronically delivered version of
"Simulation in the Service of Society" which is a special section of
SIMULATION, a monthly technical journal of the  Society for Computer
Simulation International. It may be reproduced only for personal use
or for the use of students. In any case full credit must be given to
the original source of publication: SIMULATION  59:5, November 1992.
    All rights  reserved, (c) 1992, Simulation Councils, Inc.]
 ----------------------------------------------------------------------
 
A Simple, Globally Aggregated, Stochastic-Simulation Model
 
In an attempt to enlist the participation of recognized
contributors to the solution of the problems of our environment,
your Ed. invited Paul Ehrlich to join us in a session on the
environment at one of our conferences.  He politely declined,
pleading being "snowed" (a situation which most of us can
understand), but he sent us a copy of a paper that he co-authored
that certainly has, in our opinion, some thoughts worth passing
on.
 
Because "Simulation in the Service of Society" is not a technical
publication we will skip the seven 16-column tables of output
data and their detailed discussion, and bring you only what we
consider extremely pertinent (i.e., life- and death-related)
excerpts.
                           *        *
 
   An Exploratory Model of the Impact of Rapid Climate Change
                   on the World Food Situation*
 
              Gretchen C. Daily and Paul R. Ehrlich
                Department of Biological Sciences
                       Stanford University
                      Stanford, California
 
*Published in Proceedings of the Royal Society London, vol. 241, 1990.
 
The Model
 
The model simulates the effect of stochastic perturbations in
food production (due to climate change) on population size.  In
yearly increments, the model calculates human population size,
number of hunger-related deaths, and the production, consumption
and storage of grain under different climatic scenarios. 
Parameters that may vary in each run of the model include the
initial population size, the initial level of grain production
and grain stores, the rate of change in population size and grain
production, whether climate change has a net favourable or
unfavourable impact on global agricultural production, the
frequency and magnitude of changes in global harvest because of
changed weather patterns, the projection time, and the desired
number of simulations.  The climate scenarios are described in
terms of two parameters: the frequency and the magnitude of
changes in global grain production caused by changing weather
patterns.  All of the parameters in the model represent
aggregates for the world as a whole.
 
Global aggregation of the model is a serious limitation. 
Geographic variation in weather tends to make gluts or shortfalls
of grain regional events whose consequences can, at least in
theory, be compensated by trade.  When the highly aggregated
"Limits to Growth" model (Meadows et al. 1972) was re-run at
regional levels (Mesarovic and Pestel 1974) it was found to have
overestimated global disaster but underpredicted regional
disasters.  However, because of the uncertainties of modelling
climate (especially at regional levels), the changing patterns of
international grain trade, and the functioning of futures
markets, disaggregating our model did not seem a wise course. 
Instead, we attempted to capture some of the complexities of
regional variations in our parameterization of mortality relative
to global grain stocks.
 
The manipulations of the input parameters were shown in a flow
diagram of the model (Figure 1 of the published paper).  The
initial values of input parameters for population and
agricultural production were selected from recent but not extreme
years.
 
In the following, delta N is the annual rate of natural increase
of the population size, and delta G is the annual growth in grain
production.  The initial population size and growth rate were set
at 5.000 billion and 1.7% per year, respectively, figures that
roughly reflect conditions in 1986, the year of peak grain
harvest to date.  Population size may be sharply reduced by grain
shortages (which cause rapid increases in deaths by starvation). 
These periods of population decrease are assumed to be
instantaneous.  Following such periods, the constant rate of
increase is applied to the new lower population size.  The
feedback between low or empty grain stocks and population is
described below.
 
For most scenarios, initial production was set at 1.65 billion
metric tons (T) grain, roughly the amount that was consumed in
1986.  The underlying rate of change in grain production (the
"trend") also remains constant.  For reference, the average value
of the trend was 2.6% per year from 1969 to 1979, and 1.4% per
year from 1980 to 1988 (Food and Agriculture Organization 1970-
89). 
 
Results
 
The output of the model under a variety of scenarios was
displayed in Tables 1-7 and summarized in Figure 2 [not
reproduced here].
 
Discussion
 
The complexity of the systems that interface in this model,
including population, agriculture, and climate, not to mention
economics, trade, government policy and international
relations, make it impossible to quantify accurately the interactions
between them.  Nonetheless, the results of our relatively simple
model have heuristic and perhaps some predictive value.  They
offer insight [emphasis JM] to the vulnerabilities of our
agricultural system and growing population, and provide a measure
of the relative importance of key factors in the population-food-
climate interaction.
 
(a)  Limitations of the model
 
The model has several important limitations.  First, it accounts
for regional heterogeneity only by including deaths caused by
maldistribution.  This is a crude approximation because
inequitable distribution of food (and wealth in general) and
extreme heterogeneity in population density, in agricultural
productivity (over space and time), in climate regimes, and in the
variability of weather patterns, are key factors in generating
regional famine.  For example, at least since 1966, the world has
never experienced a global grain deficit (by our standard of 0.33
T per person per year) yet hunger has afflicted local and
regional populations repeatedly throughout this period.
 
Secondly, the model does not include mechanisms whereby
compensation for imminent food shortages could be made.  Such
mechanisms include (1) spurring research and development of new
technology and crop strains to increase yields; (2) intensifying
crop production with increased inputs of water, fertilizer and
pesticides; (3) bringing set-aside and other marginal land in the
USA and elsewhere into production; (4) consuming more crops
directly (as opposed to feeding livestock); (5) reducing herds by
temporarily consuming more livestock (which represent a large
food reserve); (6) reducing wastage between farm and stomach; and
(7) development and implementation of emergency relief measures
to minimize and contain the effects of local crop failures.
 
How likely are such ameliorating factors to make major impacts? 
The potential effectiveness of mechanism (1) is debatable;
although no bright technological prospects lie on the immediate
horizon, there is evidence in support of the hypothesis that
crises stimulate innovation.  Mechanisms (2) and (3) are likely
to provide only short-term relief, and to be detrimental in the
longer term; these traditional methods of expanding agricultural
production are very resource-intensive, generally not
sustainable, and rapidly approaching physical constraints. 
Mechanisms (4)-(7) are critical steps towards buffering over-
large populations from the most devastating effects of
insufficient production.  All of these mechanisms may operate to
reduce the number of deaths predicted by the model, but none
represents a long-term solution to the problem of delta N
outstripping delta G.
 
Thirdly, the model implicitly assumes that the underlying 'trend`
(rate of change) in grain production will remain constant even in
the face of the social and economic turmoils sure to result from
massive crop failures, severe famine. loss of habitable land in
coastal areas and other impacts of unfavourable climate change. 
Furthermore, maintaining a growth rate in agricultural output of
1.7% per year embodies a series of optimistic assumptions of
success in the development and implementations of better
agricultural practices and technologies.  In addition, the
effects of climate change are assumed to be constant, when really
they may intensify.  These assumptions would all have the effect
of under-estimating the number of deaths that may result from the
impacts of deleterious climate change.
 
Fourthly, the number of deaths produced by the model under
different scenarios depends to a large degree on the factor used
to convert grain deficits to deaths.  Currently, three people are
supported on average by each tonne* of grain produced per year. 
 
*Metric ton, 2352 pounds
 
However, 1 T grain per year delivered to the mouth can provide
four adults with 'adequate` diets and five adults with
'subsistence` diets.  The brunt of any deficits is likely to be
borne by the world's poorest people; in response to the same
decrease in supply, the poor reduce their grain consumption more
than ten times as much as the wealthy, who simply forego luxury
items.  At one extreme, a few tonnes deficit in a rich country
would probably not cause any deaths, whereas at the other, a one-
tonne deficit in a poor country might cause the deaths of four
subsistence-diet adults and two children.  Considering these
various factors and uncertainties, we feel an estimate of two
deaths per tonne deficit is conservative.
 
Finally, a few comments relative to our validation of the model
must be made.  It is very difficult to quantify the actual number
of people that have starved to death over the past two decades. 
Aside from poor censusing in famine-stricken areas, malnutrition
compromises the immune system, and the immediate cause of death
of severely malnourished people is thus usually reported as
disease.  The rough estimate of 200 million deaths is
considerably higher than the average of 100 (+ 30) million deaths
per simulation produced in our test scenario that approximtes
conditions over those decades.  The numbers of deaths produced by
the distributional aspects of the model are therefore probably
conservative.
 
Conclusions
 
Four general conclusions can be drawn from the model regarding
the number of people at risk of starvation and the importance of
the relation between delta N and delta G to both the creation of
deficits and the relative impact of unfavourable climatic
conditions. 
 
First, the model suggests that several hundreds of millions to a
billion or so people could die of hunger in future decades. 
Examinations of the pattern of deaths within runs suggest that
such numbers of people are not likely to die in a single large
famine.  Those additional deaths would not, however, be primarily
'distributional`, as have been those of the past two decades. 
Instead, the vast majority of them could be due to absolute
global shortage.
 
The second primary conclusion from the model is that seemingly
small (on the order of 0.3%) differences in the annual rates of
growth in population and agricultural production can have a large
impact on global food security.  This is an important point
because land degradation (in the form of soil erosion,
waterlogging and salinization of irrigated land, and decline in
soil fertility), scarcity of fresh water in many parts of the
world, and the increasing cost of fertilizer and pesticide
inputs, threaten to constrain growth in grain production.
 
The model highlights the effectiveness of declines in population
growth toward minimizing the impact of deleterious global climate
change and providing food for everyone.  Initiation of the socio-
economic changes required to reduce birth rates is critical to
bringing the human population to a size compatible even with the
short-term carrying capacity of Earth.
 
Thirdly, the model produced the interesting result that climate
change contributes proportionally much less to food deficits when
population growth outpaces growth in agricultural production
than when growth in each is equal.
 
Though the results under scenarios leading to high mortality may
appear roughly the same (hundreds of millions of deaths) the
distribution of deficits between rich and poor nations may be
quite different.  If the main cause of deficits is that growth
in population size exceeds 'trend` growth in grain production,
then the swelling populations in the Third World will be most
directly affected.
 
Finally, it is conceivable that CO2 fertilization and concomitant
reduced transpiration will enhance global agricultural
productivity.  In our view, however, the potential benefits of
CO2 fertilization are likely to be outweighed by the negative
impact of changing temperature belts, reduced water
availability in major grain-growing regions, possible
unfavourable changes in crop-pest relations, and social and
economic disruptions.
 
As to be expected, when production keeps pace with population
growth, relatively few deaths occur (and those as a result of
maldistribution). However, if population growth outpaces
production by 0.8% per year or so, then even very favourable
climatic scenarios (e.g. with 5 or 10% increases in production
every three-five years), do not prevent the deaths of over 800
million people on average over a 20-year period.
 
The model highlights the delicate balance between the nutritional
needs of a rapidly growing human population and the ability of
Earth to sustain the food production required to meet those
needs.  The odds that climate change will produce a net benefit
to global agriculture seem small.  But even if the odds of
favourable and deleterious impact were even, the model suggests
trying to slow the climatic change, since if delta N = delta G,
many more lives would be lost if the changes were harmful than
would be saved if they were beneficial, especially as the
climatic impact (probability of event times magnitude of change)
increases.
 
Analysis of the model further suggests that humanity may face a
situation unprecedented in the modern era: absolute global food
shortage.  The political, economic and social consequences of
such a situation in a "global village" are difficult to imagine. 
That possibility presents itself at a time when conventional
methods of expanding food production may be reaching physical and
economic limits.
 
For the immediate future, global food security could be increased
by minimizing the amount of food wasted between harvest and
consumption, by strengthening the agricultural sectors of poor
nations, and by improving the equity of food distribution. 
Long-term food security can ultimately be achieved only by
initiating the socio-economic changes necessary to bring about
reduced birth rates.  In addition, the model reinforces the
prudence of striving to reduce the emission of greenhouse gases
into the atmosphere (which in turn is strongly driven by
population growth).  It also supports the view that providing
everyone with adequate diets will remain a tremendous challenge
even without the threat of global climatic change.
 
                       *        *        *



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