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CMSC 828N, Game Theory.
Spring semester, 2009.
Instructor: Dana Nau <http://www.cs.umd.edu/~nau>

This course will be a seminar, hence it will not count as a core course for
the CS PhD or MS requirements - but it can be used as an elective.  Dr. Nau
will probably use a textbook for some of the introductory material - but
after that point, much of the material will be papers from the current
literature. 

The primary workload for the students will be
- reading and presenting papers
- written homework assignments, and possibly some short programming
exercises
- course projects, on topics chosen by the students

Here are some of the topics to be covered. Other additional topics will also
be covered; these will depend partly on the interests of the students.

Zero-sum games.  This class of games includes well-known board games and
card games such as chess, checkers, bridge, poker, and the like.  AI
researchers have done much work on such games, resulting in well-known
techniques such as minimax search, alpha-beta pruning, etc.

Non-zero-sum games. Such games have been of great interest to social and
behavioral scientists as models of various phenomena occurring in economics,
human culture, and biological evolution.  We'll look at several examples of
such games, at the notion of "rational" behavior, and at various kinds of
game-theoretic equilibria (dominant, Nash, subgame-perfect, evolutionarily
stable, etc.).

Evolutionary game theory.  This originated as an application of the
mathematical theory of games in biological contexts, arising from the
realization that frequency dependent fitness introduces a strategic aspect
to evolution. But it has become increasingly interesting to researchers in
other areas, because it provides some important elements missing from
classical game theory. It differs from classical game theory by focusing on
the dynamics of strategy change more than the properties of strategy
equilibria. 

Agent modeling. The behavior of the agents who participate in a game can
differ substantially from game-theoretically "rational" behavior, especially
in imperfect-information games and non-zero-sum games.  We'll look at
several kinds of situaions where this happens, and at ways to construct
models of the players that forecast their actual behavior (as opposed to
ideal rational behavior).

Mechanism design. This is how to design the rules of a game or system, so
that the self-interested behavior of each agent will lead toward some kind
of socially desirable outcome. It is related to metagame analysis, which
uses the techniques of game theory to develop rules for a game.


Professor Dana S. Nau
Department of Computer Science, and Institute for Systems Research
University of Maryland, College Park, MD 20742
phone 301-405-2684    fax 301-405-6707    http://www.cs.umd.edu/~nau




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<DIV dir=3Dltr align=3Dleft>CMSC 828N, Game Theory.</DIV>
<DIV>
<DIV>Spring semester, 2009.</DIV>
<DIV>Instructor: Dana Nau &lt;<A=20
href=3D"http://www.cs.umd.edu/~nau">http://www.cs.umd.edu/~nau</A>&gt;</D=
IV>
<DIV><BR></DIV>
<DIV>This course&nbsp;will be a seminar, hence it will not count as a =
core=20
course for the CS PhD or MS requirements - but it can be used as an =
elective.=20
&nbsp;Dr. Nau will probably use a textbook for some of the introductory =
material=20
- but after that point, much of the material will be papers from the =
current=20
literature.&nbsp;</DIV>
<DIV><BR></DIV>
<DIV>The primary workload for the students will be</DIV>
<DIV>- reading and presenting papers</DIV>
<DIV>- written homework assignments, and&nbsp;possibly some short =
programming=20
exercises</DIV>
<DIV>
<DIV>
<DIV>
<DIV>- course projects, on topics chosen by the=20
students</DIV><BR></DIV></DIV></DIV>
<DIV>Here are some of the topics to be covered. Other additional topics =
will=20
also be covered; these will depend partly on the interests of the=20
students.</DIV>
<DIV><BR></DIV>
<DIV><B>Zero-sum games</B>. &nbsp;This class of games includes =
well-known board=20
games and card games such as chess, checkers, bridge, poker, and the =
like.=20
&nbsp;AI researchers have done much work on such games, resulting in =
well-known=20
techniques such as minimax search, alpha-beta pruning, etc.</DIV>
<DIV><BR></DIV>
<DIV><B>Non-zero-sum games</B>. Such games have been of great interest =
to social=20
and behavioral scientists as models of various phenomena occurring in =
economics,=20
human culture, and biological evolution. &nbsp;We'll look at several =
examples of=20
such games, at the notion of "rational" behavior, and at =
various&nbsp;kinds of=20
game-theoretic equilibria (dominant, Nash, subgame-perfect, =
evolutionarily=20
stable, etc.).</DIV>
<DIV><BR></DIV>
<DIV><B>Evolutionary game theory</B>. &nbsp;This&nbsp;originated as an=20
application of the mathematical theory of games in biological contexts, =
arising=20
from the realization that frequency dependent fitness introduces a =
strategic=20
aspect to evolution.&nbsp;But it has become increasingly interesting to=20
researchers in other areas, because it&nbsp;provides some important =
elements=20
missing from classical game theory.&nbsp;It=20
differs&nbsp;from&nbsp;classical&nbsp;game&nbsp;theory&nbsp;by&nbsp;focus=
ing&nbsp;on&nbsp;the&nbsp;dynamics&nbsp;of&nbsp;strategy&nbsp;change&nbsp=
;more&nbsp;than&nbsp;the&nbsp;properties&nbsp;of&nbsp;strategy&nbsp;equil=
ibria.&nbsp;</DIV>
<DIV><BR></DIV>
<DIV><B>Agent modeling</B>. The behavior of the agents who participate =
in a game=20
can differ substantially=20
from&nbsp;game-theoretically&nbsp;"rational"&nbsp;behavior, especially=20
in&nbsp;imperfect-information games and non-zero-sum games. &nbsp;We'll =
look at=20
several kinds of situaions where this happens, and at ways to construct =
models=20
of the players that forecast their actual behavior (as opposed to ideal =
rational=20
behavior).</DIV>
<DIV><BR></DIV>
<DIV><B>Mechanism design</B>. This is how to design the&nbsp;rules of=20
a&nbsp;game&nbsp;or system, so that the self-interested behavior of each =
agent=20
will lead toward some kind of socially desirable&nbsp;outcome. It is =
related to=20
metagame analysis, which uses the techniques of game theory to develop =
rules for=20
a game.</DIV>
<DIV><BR></DIV></DIV><BR>
<DIV apple-content-edited=3D"true">
<DIV>
<DIV>
<DIV>
<DIV>
<DIV>Professor Dana S. Nau</DIV>
<DIV>Department of Computer Science, and Institute for Systems =
Research</DIV>
<DIV>University of Maryland, College Park, MD 20742</DIV>
<DIV>phone 301-405-2684&nbsp; &nbsp;&nbsp;fax 301-405-6707&nbsp; =
&nbsp;&nbsp;<A=20
href=3D"http://www.cs.umd.edu/~nau">http://www.cs.umd.edu/~nau</A></DIV>
<DIV><BR></DIV></DIV></DIV></DIV></DIV><BR></DIV><BR></BODY></HTML>

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