[AISWorld] Course content re computers, simulations, models, decision making, software and model testing?

[Swartz, Steve]

Leon:



Indeed . . . I will give it some thought.  Perhaps we can sit down over coffee (or stronger!) and chat.



In the meantime, first thoughts:



- Early on I took a "theory of science" class as part of an engineering curriculum at Big State U.  I remember the discussion of modeling (closed form and discrete system) and models.  Picture a Venn digram with two "mostly" overlapping geometric forms.  One is ugly, with ill-defined edges, fuzzy borders that trail off in palces, and mysterious tentacles and voids.  Reality.  The other shape is a nice tetrahedron made up of irregular, but clean and straight sides (alle es en ordnung).  The Model.  The professor (classic 1950s grey buzz cut, bowtie or string tie, pocket protector, NASA-style Vuarnets, the whole 9 yards) kicked off the discussion this way:  "There are only two problems with drawing inferences from models.  First, there are things the model does, that do not exist in reality.  Second, there are infinitely many more things that reality does, that the model does not.  Analog, Digital, Iconic, Dynamic, Static, Adaptive, or whatever.  Gentlemen, when you work with models you are F'd [ed note: yes, an engineering professor dropped the F-Bomb in a 1981 classroom].  And worse yet- models look like controlled laboratories, smell like controlled laboratories, and might quack like controlled laboratories- but never forget they are only laboratories of the imagination and conjecture of their designers.  So let's talk about building good models."



- Many years later in my doctoral program (different Big State U) I took a class called "Lying With Statistics."  [I think the real title was "processing and display of analysis" or some such]  Many of us have had classes like that; use Pauli's (?) "A Mathematician Reads the Newspaper" as a textbook and demonstrates myriad ways to construct tables, graphs, etc. and write compelling fiction about sets of data.





The advantage of spending so much time in the classroom as a student in various disciplines is that we see some really, really, great ways of communicating critical thinking perspectives that can address the root of problems like these [Asimov wrote some great stuff about "layman-izing" technical material "for the masses"].  The downside is we also can appreciate how rare these great teachers and lessons can be.



I'm assuming, of course, the "root of the problem" is the inability of the average journalism school graduate to communicate complex technical subjects to an equally woefully unprepared public.  And the inability of consumers of technical communications to properly draw inference from the results and findings presented; ie properly calibrated BS Detectors.



And yes, self-promoting charlatans in the scientific community who prey on the weaknesses of a frightened and ignorant public to line their own pockets and serve their own agendas.  That's probably part of the problem too.


Stephen M. Swartz, PhD, CTL
Associate Professor of Logistics Management
Department of Marketing and Logistics
University of North Texas
1155 Union Circle #311396
Denton, TX 76203-5017
vox:  (940) 565-3673
fax:  (940) 565-3837

________________________________
From: Kappelman, Leon
Sent: Friday, July 29, 2011 3:26 PM
To: aisworld at lists.aisnet.org
Subject: Course content re computers, simulations, models, decision making, software and model testing?

So many important public policy decisions have significant myriad long-term effects on life on this planet.  This is particularly true of macro economic, health, and environmental decisions such as the current efforts by the US federal government involving fiscal, health, and environmental matters.  Most all of these decisions, as well as many significant ones made by organization managers, are based on models grounded in various theories, assumptions, and estimates.  Most of these models are operationalized through some form of computerization and then important decisions are made.  Once the computers run, most decision makers, and certainly the public at large, take the models to be reality.  Those of us trained in research may realize that there are merely theory-based simulations, but I do not recall ever hearing a probability estimate with projection based on such a simulation involving a government budget or environmental policy, and pretty rarely in any other context except academic research settings.  So, I’m curious to know, is anyone teaching awareness of these critical, and often misused, roles of computers in a computer- or MIS-literacy survey course, or in a course for our graduate or undergraduate majors, or in research courses for our PhD students.  And if so, would you please share such materials with me and/or the list?  I know statistics are taught and that simulation courses exist, but it’s the combination and its implications in important decision making that I am trying to better incorporate into my own teaching.

What prompted this question is the example that follows regarding the use of such computerized models in an important public policy matter.  Recent research (http://www.mdpi.com/2072-4292/3/8/1603/pdf) suggests the theories behind the simulations are erroneous.  Nothing new there in terms of how science progresses; but this particular area of public policy has already involved many hundreds of billions of dollars in impacts on economic and environmental public policies across the globe.  Indigenous peoples have been displaced, billions of dollars in taxes raised, industries impacted, businesses shuttered, jobs lost but some others created, prices increased, and billions in tax dollars spent subsidizing solutions to problems that in fact may not even exist and often create real problems.  So the question that crossed my mind was, what might I teach (or perhaps write) that may help prevent scientists, computer professionals, and decisions makers from making such blunders, and the public at large from accepting them so readily, and hence my question to the list.

But please do not take this posting as an opportunity to debate climate theory or the particulars of public policy on this list.  I assume everyone who reads this note wants to be a good steward of the environment in the very best way they can.  These are pedagogical questions I am asking, not a public policy or climate theory ones.

Hope all is good with you all this summer!

Best wishes,
Leon

“The dogmas of the quiet past are inadequate to the stormy present....  As our case is new, so we must think anew, and act anew.” -- Abraham Lincoln, message to Congress, Dec. 1, 1862, just before issuing the Emancipation Proclamation.


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Leon A. Kappelman, Ph.D.
  Professor of Information Systems
  Director Emeritus, Information Systems Research Center
  Fellow, Texas Center for Digital Knowledge
    College of Business, University of North Texas
    Voice: 940-565-4698   Fax: 940-565-4935  Email: kapp at unt.edu<mailto:kapp at unt.edu>
    Website: http://courses.unt.edu/kappelman/
Founding Chair, Society for Information Management's Enterprise Architecture Working Group
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Please consider the environment before printing this e-mail

Subject: New NASA data challenges global warming models
Like the 5-day weather forecast, the 100-year ones are also prone to error.  In fact, more so given their added complexity and the duration of the simulation.  From the report below: "'There is a huge discrepancy between the data and the forecasts that is especially big over the oceans.'  Not only does the atmosphere release more energy than previously thought, it starts releasing it earlier in a warming cycle.  The models forecast that the climate should continue to absorb solar energy....  Instead, the satellite data shows the climate system starting to shed energy more than three months before the typical warming event reaches its peak.”
>From http://www.kurzweilai.net/new-nasa-data-challenges-global-warming-alarmism
New NASA data challenges global warming alarmism
July 29, 2011
NASA satellite data show the Earth’s atmosphere is allowing far more heat to be released into space than alarmist computer models have predicted, according to a new study in the peer-reviewed science journal Remote Sensing.  [http://www.mdpi.com/2072-4292/3/8/1603/pdf]
Data from NASA’s Terra satellite shows that when the climate warms, Earth’s atmosphere is apparently more efficient at releasing energy to space than models used to forecast climate change have been programmed to “believe.”
The result is climate forecasts that are warming substantially faster than the atmosphere, says Dr. Roy Spencer<http://www.uah.edu/news/newspages/campusnews.php?id=564>, a principal research scientist in the Earth System Science Center at The University of Alabama in Huntsville.
In research published in the journal Remote Sensing, Spencer and UAHuntsville’s Dr. Danny Braswell compared what a half dozen climate models say the atmosphere should do to satellite data showing what the atmosphere actually did during the 18 months before and after warming events between 2000 and 2011.
“The satellite observations suggest there is much more energy lost to space during and after warming than the climate models show,” Spencer said. “There is a huge discrepancy between the data and the forecasts that is especially big over the oceans.”
Not only does the atmosphere release more energy than previously thought, it starts releasing it earlier in a warming cycle. The models forecast that the climate should continue to absorb solar energy until a warming event peaks.
Energy lost, not gained: satellite data
Instead, the satellite data shows the climate system starting to shed energy more than three months before the typical warming event reaches its peak.
“At the peak, satellites show energy being lost while climate models show energy still being gained,” Spencer said.
Applied to long-term climate change, the research might indicate that the climate is less sensitive to warming due to increased carbon dioxide concentrations in the atmosphere than climate modelers have theorized. A major underpinning of global warming theory is that the slight warming caused by enhanced greenhouse gases should change cloud cover in ways that cause additional warming, which would be a positive feedback cycle.
Instead, the natural ebb and flow of clouds, solar radiation, heat rising from the oceans and a myriad of other factors added to the different time lags in which they impact the atmosphere might make it impossible to isolate or accurately identify which piece of Earth’s changing climate is feedback from manmade greenhouse gases.
“There are simply too many variables to reliably gauge the right number for that,” Spencer said. “The main finding from this research is that there is no solution to the problem of measuring atmospheric feedback, due mostly to our inability to distinguish between radiative forcing and radiative feedback in our observations.”
For this experiment, the UAHuntsville team used surface temperature data gathered by the Hadley Climate Research Unit in Great Britain. The radiant energy data was collected by the Clouds and Earth’s Radiant Energy System (CERES) instruments aboard NASA’s Terra satellite.
The six climate models were chosen from those used by the U.N.’s Intergovernmental Panel on Climate Change. The UAHuntsville team used the three models programmed using the greatest sensitivity to radiative forcing and the three that programmed in the least sensitivity.
If you’d like to learn more about such climate models, there is a well-documented one in the report by MIT’s Joint Program on the Science and Policy of Global Change that is linked from this commentary https://courses.unt.edu/kappelman/blog/content/weak-underbelly-climate-models-are-you-feeling-lucky-punk.


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