Download Notes for white House Briefing - Stony Brook Center for Game Theory

Game Theory to the Real World
Using the Media Feedback Loop to Directly Measure Changes
in Perceived Utility
-- DRAFT -By Timothy J. Kaelin
Game Theory is the theory of rational behavior by two or more interacting individuals
each determined to maximize his own expected gain as define by his own utility function.
The term expected has a connotation of perception, and indeed, ‘belief’ since what may
actually occur after the decision could be entirely unexpected. Since the real world is
incredibly complex, human beings can rarely construct a complete and accurate causeeffect view, but rather must rely on the information at hand and then judge the credibility
of conflicting information. Beliefs and perceptions can be manipulated, and the
probability that any decision will be taken in a game situation can change, even when
the facts and potential outcomes do not.
We take this further and say that perceived utility is not a matter of factual usefulness,
but rather a summation of psychological influences that result in the perceived utility. In
his work, Harsanyi (Advances in Understanding Rational Behavior) describes the
problem of analyzing human behavior in the face of uncertainty, and, in another work,
the usefulness of cardinal utility in risk situations. In each case one must conclude that
utility is relative to the beholder and new information can lead to a change in the
perceived utility of a given decision. Let’s illustrate by considering the ‘law’ of supply
and demand and how economists express the theoretical linkage between a price,
quantity and product utility. Every business has a marketing team whose job is to defy
this law and increase the demand regardless of other factors. This manipulation of the
variables is done by using marketing, mass communications and propaganda
techniques to change the perceived good in the product (i.e. increase demand, decrease
that of a competitor), change the perception of the quantity availability (Get It While
Supplies Last!!!), or change the perception of the reasonableness of the price.
This paper proposes a measurement system to directly measure the psychological
influences of the media on a given issue with the intention of providing changes in
probabilities suitable for game theory use. The paper is qualitative in nature, and while
we have performed much of the mathematical treatment and software development for
modeling purposes, they are not presented here. However, this paper will be
accompanied by a computerized simulation of a macro social application related to
emergency management, presenting an hour by hour complex model with feedback loop
illustration and opportunities to interfere and circumvent public opinion.
Game theorists apply marginal utility as a quantity or a probability, This paper bridges
that probability with the psychological influences that are the cause of that probability in
the first place. Using McMillan’s theories of the unification of the social sciences, I will
provide a path to measure changes in utility in terms of changes in positive/negative
range with relation to a given decision. Positive/negative can be described as a
psychological measure, thus we will use the rules created for mass communications,
marketing and propaganda (please forgive the use of this term, we refer to the
scientifically derived techniques without assigning moral value) to form a structure for the
mass measure of positive/negative attitude changes with directly correlate to perceived
utility. (Harsanyi)
The Evolutionary Basis
Man’s greatest asset is his ability to reason, to think in the abstract, he’s able to draw
conclusions even in the face of incomplete information. The downside is that he often
will draw conclusions even in the face of incomplete information, with insufficient facts,
using past perceptions to make future judgments. He will project himself into other
people’s situations and empathize completely. A man’s mind is free to distort reality to
the limits of his imagination, and he will act on his conclusions. These patterns of
thinking provide the basis for propaganda as practiced over the past centuries and
developed into a science in the first half of the twentieth century.
Perception is a combination of knowledge (which may or may not be complete and may
or may not be truth), emotion (which distorts current knowledge), and internal processing
(the lens through with this knowledge is evaluated).
Only by knowing the communications channels and the psychological influences can
one begin to measure the perceived utility in a given circumstance and make an
estimate of the probabilities in any decision making situation. Conversely, by controlling
or dominating the communications channels one can obviously (obvious to marketing
people, politicians and propagandists at least) change the perceived value of an opinion,
decision or course of action.
Mr. George McMillan (ref) has developed a unifying theory for the economics, political
theory and social development, stating, in part, that they are psychologically based and
that psychology should be used as a base operating language. This paper illustrates
how one can tap into and quantify the constant stream of psychological influences
encountered in mass media, quantify these influences in a single comparative
framework, use them as input into game theoretical models, thus providing the potential
to link models to the real and ever-changing world.
The media feedback loop breaks down the basic communications links to a simplified
form that can be expanded and replicated on a large scale. We implement this loop by
modeling the component, the Decision Maker, the interface from media to the Decision
Maker, and the interface from Decision Make to mass media, and the response of Mass
Media. Note in the diagram below the Decision Maker represents the influenced party.
In its practical application, the
Ally, Rival Population
Actions and Reactions
Acts of God
Unrelated, Overshadowing
Events, “Noise”
Commentary Inputs
Mass Media
Mass Communications
Reporting of
Decisions
Direct Influence
by advisors
Decision Maker
Culture
Decisions
Evaluation of Influences
Decision Processes
Experience
Education
The Media/Decision Maker Interface
We define the Decision Maker as a demographic that can be counted on to respond to a
particular issue in a similar manner. We do not use the phrase “counted on” lightly, a
vetting methodology has been developed, but is outside the scope of this paper. Suffice
it to say that the processes can be mechanized with some accuracy and efficiency.
The upper half of the loop in Figure 1 represents the flow of mass media influences.
One can intuitively consider the impact of mass media on public opinion, while
considering that each individual has the freedom to form their opinion. This is
misleading however, in light of the mechanics of propaganda. In actual fact, unless an
individual has personal knowledge of an event or a unique source of relevant data, the
opinions are not “formed”, they are chosen from an array of opinions that are presented
in mass media sources. Further, propaganda theory states that persistent mass media
is ultimately persuasive, unless the content conflicts with deeply in-grained values. All
Decision Makers do not respond equally, and each will be influenced heavily, but not
exclusively, by media sources that cater to them. Each media influence can be roughly
weighted for each Decision Maker, however it would be a bit cumbersome to attempt to
gauge the coefficients for each individual in a demographic. The smallest practical unit
to deal with in this case becomes obvious with even a cursory knowledge of the how
media economics work. Media survives on advertising and an essential part of selling
advertising is to describe the demographics of the source’s audience. Therefore the
“Decision Maker” unit should look familiar, “men 18-34, “senior citizens”, “black males in
the Mid-West”, “computer users over 50”, etc, depending on the availability of the
demographic breakdowns and the relevance to the subject under study.
Determining the correct demographic unit is crucial since different demographics will a)
receive different “knowledge” from their affinitive sources, b) be moved to different
emotional states (or no emotional state), depending on the how their situations are
affected, and c) may come to different conclusions even when presented with exactly the
same facts. In the Loop diagram, the “processing” phase is represented in the bottom
half. Culture, education, and experience are important demographics characteristics
that must be considered. However, we expect to find that demographic breakdowns are
fixed for each media source, and that developing additional ones would be difficult if not
impossible.
From a standpoint of analytical methodology, we attempt to work with the smallest
demographic for which we can get sufficient data. Propaganda theory says that each of
these demographics will react differently to the various stimuli and that internal
processing and internal motivations must be calculated independently for each
demographic. However, once these internal processes are measured, the external
reactions can be treated in a polling fashion and the results added numerically to get the
total public reaction (where a “total” reaction is sought). Other times, a strong reaction in
an individual demographic may be of more interest, where an issue is less relevant to
the other demographics.
When attempting to quantify an individual demographic response we look to build from
quantifiable characteristics that are analogous to communications engineering
terminology. We rely on this analogy heavily, but these can be qualitatively describes
in scientific propaganda terms. The result of the model will be a scale of success of
competing influences, and the resulting opinion and reaction of that success, based on
historical data.
Signal to Noise Ratio – While a influencing topic may be relevant, and might potentially
stimulate a reaction in a demographic unit, it must stand out from the wide variety of
stories and opposing signals that are aimed at the demographic. The signal must impact
the demographic with strength and over time. If the signal does not capture the focus or
if a variety of opposing (“out of phase”) signals or neutral signals on the same subject
are present, or if the signals are buried in irrelevant material, then the signal to noise
ratio may be too low to have an effect, or may take much longer to take effect. These
are measure for each demographic.
Interfering Signals – As opposed to “noise”, interfering signals are measured more often
with respect to the media as a whole, not the individual demographic. These are currents
of influence that grab the focus and overwhelm lesser influences. For example, a major
oil spill would interfere with, but perhaps not eliminate, an ongoing debate about the
spotted owl.
Figure 2 – Feedback Loop quantitative implementation
Filtering – A variety of filters are used, it is necessary to filter a current of influence to the
narrowest possible definition to get the most precise measurements of reaction.
Another example of filtering is in term of credibility of a news source, for which we can
assign a credibility coefficient relating to each demographic.
Amplification – We generally apply this to a mass media source that caters to one
particular demographic. For this demographic, influence through this media will be
amplified, providing a much better signal to noise ratio (i.e. a clearer, more influential
signal). This is actually very similar to a filtering process, and, as in communications
engineering, the result in either case is an improvement in signal to noise ratio.
Propagation Path - We talk about propagation path in terms of delay time, strength of
issue. The arrival time of an influence should be less important than
Figure 3 - Mass Media has various characteristics that can be modeled numerically.
Transmitter -- The mass media source, represented as a transmitter must be
characterized for reach, in terms of circulation, affected geography and targeted
demographic. For each demographic, a credibility coefficient must be determined,
which becomes part of the filter for that demographic with respect to the media source.
Additionally, the placement of the relevant influence within the media source is an
additional determinant of audience reach, e.g. above the “fold”, first page of a web site,
opening story of a broadcast The geographic element provides an obvious
discriminative element.
Receiver – The demographic can be modeled quantitatively as a receiver, with
appropriate filters on the input based on known reactive characteristics and media
preferences. The internal decision processes are analogously termed “demodulators”.
Using these parts one in their analogous form it is possible to model the communications
process from media to actor. Ultimately, it is possible to use an interactive, computerbased process to build a framework for the timing and impact of an influence pattern with
relatively modest effort (likely hours for an experienced analyst) if the proper databases
of media have been built (both content and meta data), and the demographic patterns
for a region are known.
The Decision Maker/Mass Media Interface
The communications path from Decision Maker to Mass Media is complex, but has been
described in detail by various sources. One major driving factor in Mass Media is
economic, reporters are trained to know what is of interest to their readership and report
what will attract readership. Chomsky (33) does a reasonable job of describing the
filtering process in mass media,
The resulting analytical study can store all of the cause-effect calculations and
assumptions, and provide for drill-down to the original source material, including the
body of news articles, filtering calculations, media influence characteristics, and any
proprietary data that might be used. Since influences are translated into numerical form,
the results can be tested for sensitivity to any individual factor, and random factors can
be introduced. The results can be displayed on a moving map display (a “weather
map”, on a timeline, through a link chart, or any interactive combination. This assists
greatly with the interface between analytical expertise (economic, political, social), since
all influences are modeled in the same framework and assumptions are concrete. All
influences are described both quantitatively and qualitatively, unsuccessful models can
be discussed and areas of failure address specifically.
The process allows for more advanced filters, rather than studying a particular topic,
filters can be designed to follow tension characteristics and general unrest in the media.
In an area where tension may translate into unrest in the form of war, rioting, genocide,
etc.
It is anticipated that studies can be combined into more complex analytical frameworks,
and used for the analysis of policy alternatives. Using the media as the means of
propagation path, one can model the timing and impact of a source of influence as it
would travel around the world, based on past propagation studies. It is also possible to
insert autonomous agent models of the of likely actors (i.e. local leadership), based on
historical and psychological profiles, and modeled through several media cycles.
About the Author
Mr. Timothy J. Kaelin is a successful entrepreneur, and CEO of Impact Analytics where
he has pioneered a number of new analytical techniques. He holds a Master of Science
in Electrical Engineer from the University of Louisville and a Master of Arts in
International Transactions from George Mason University. He previously worked 12
years with the Central Intelligence Agency, in the fields of counter terrorism,
counternarcotics and counter intelligence.
APPENDIX
Screenshots of a Media Feedback Loop Simulation Model for Emergency
Management
Bibliography (Partial and Representative)
Communications Material – noise figure paper???
Kuo – control theory
Meme theory
Bernays, Edward. Propaganda. 1920. Current Publisher Ig Publishing, 2004.
Chomsky, Noam. Propaganda and the Control of the Public Mind. AK Audio, 2000.
Chomsky, Noam and Herman, Edward. Manufacturing Consent. Pantheon, 2002.
Durham, Meenakshi Gigi and Kellner, Douglas M. eds. Media and Cultural Studies, Keyworks.
Blackwell Publishers, 2001.
Ellul, Jacques. Propaganda, The Formation of Men’s Attitudes. Random House,1965.
Haley, Jay, ed. The Power Tactics of Jesus Christ and other essays. Avion Books, 1969
Harsanyi, John C. Rational Behavior and Bargaining Equilibrium in Games and Social
Situations. Cambridge University Press, 1977.
Harsanyi, John C. Essays on Ethics, Social Behavior, and Scientific Explanation (Essays 19531975). D. Reidel Publishing Company 1976.
Kaelin, Timothy J. An Analytical Framework for Predicting and Preventing Genocide.
Unpublished (still in draft), 2004
Kohlberg, Lawrence. The Meaning and Measurement of Moral Development. Clark University
Press, 1981.
Kossen, Stan. The Human Side of Organizaions. HarperCollins Publishers, Inc., 1991.
Krippendorff, Klaus. Content Analysis, An Introduction to Its Methodology. Sage Publications,
1980
Lasswell, Harold D. and Casey, Ralph D. Propaganda, Communication and Public Opinion.
Princeton University Press, 1946.
McMillan, George. A Unification Theory for the Social Sciences. Unpublished (still in draft), 2004
Myerson, Roger B. Game Theory, Analysis of Conflict. Harvard University Press, 1997,
Pareto, Vilfredo. A Treatise on General Sociology. Originally Pub by Harcourt, Brace and Co.,
Inc. 1935.
Popping, Roel. Computer Assisted Text Analysis. Sage Publications, 2000
Thaler, Richard H. Quasi Rational Economics. Russell Sage Foundation, 1991.