Computational Modeling of Complex Socio-Technical Systems



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Computational Modeling of Complex Socio-Technical Systems

08-810


Syllabus

Spring 2012

12 Course Units
Prof. Kathleen M. Carley

E-Mail: kathleen.carley@cs.cmu.edu

Phone: x8-6016

Office: Wean 5130

Office Hours: by appointment

T.A.: Geoffrey Morgan


Lectures: Monday, Wednesday 9:00 - 10:20 am, GHC 4211

Labs: Thursday, 4:30 - 5:20 pm, GHC 4211

PLEASE NOTE THAT THERE WILL BE NO CLASS THE WEEK OF MARCH 12 DUE TO SPRING BREAK.
All course information is available on-line via CMU Blackboard: http://www.cmu.edu/blackboard

DESCRIPTION:


We live and work in complex adaptive and evolving socio-technical systems. These systems may be complex for a variety of reasons. For example, they may be complex because there is a need to coordinate many groups, because humans are interacting with technology, because there are non routine or very knowledge intensive tasks, and so on. At the heart of this complexity is a set of adaptive agents who are connected or linked to other agents forming a network and who are constrained or enabled by the world they inhabit. Computational modeling can be used to help analyze, reason about, predict the behavior of, and possibly control such complex systems of "networked" agents.

This course is based on the simulation of complex socio-technical systems. This course teaches the student how to design, analyze, and evaluate such computational models. It will introduce several styles of simulation including agent based and system dynamics. Examples of applications of these tools to various problems such as epidemiology, organizational adaptation, information diffusion, impact of new technology on groups, and so on, will be discussed. The course should be appropriate for graduate students in all areas. This course does not teach programming. Issues covered include: common computational approaches such as multi-agent systems, general simulation and system dynamics, heuristic based optimization procedures including simulated annealing and genetic algorithms, representation schemes for complex systems (particularly, groups, organizations, tasks, networks and technology), analysis techniques such as virtual experiments and response surface mapping, docking (model-to-model analysis), validation and verification, and social Turing tests. Illustrative models will be drawn from recent publications in a wide variety of areas including distributed artificial intelligence, knowledge management, dynamic network analysis, computational organization theory, computational sociology, computational epidemiology, and computational economics.



TOPICS TO BE COVERED:


* common computational approaches such as multi-agent systems, general simulation and system dynamics * heuristic based optimization procedures including simulated annealing and genetic algorithms * representation schemes for complex systems (particularly, groups, organizations, tasks, networks and technology) * analysis techniques such as virtual experiments and response surface mapping, docking (model-to-model analysis) * validation and verification, and social Turing tests. * illustrative models will be drawn from recent publications in a wide variety of areas including distributed artificial intelligence, knowledge management, dynamic network analysis, computational organization theory, computational sociology, computational epidemiology, and computational economics.

PREREQUISITES:


The prerequisite will be basic understanding of statistics - undergraduate level.

METHOD OF EVALUATION:

Grading will be based on a set of programming assignments, validation assignments, and a major project.


Grading Breakdown





Weekly Discussion & attendance –

5% (failure to attend or discuss can make this go negative)

Assignments – 4 –

40% (10% each but failure to turn one in is -10%)


Comments on other’s presentation of final project

5% each (total 10%)

Topic Presentation

5%

Presentation of Project -

10%

Final Paper & Project –

35%

Paper & Project sub-parts (what 35% entails)



References – includes and moves beyond literature from course

Creativeness

Data – virtual or real

Justification of model

Demonstrates understanding of computational modeling concepts

Good interpretation of results

Of journal quality

Clear concise abstract

Simulation Model and Virtual Experiment Done

Organization

Good analysis

Effort, Reasonableness

Assignments turned in after the end of the term will be subject to a reduction in grade. Class members are expected to attend class, engage in discussions, read material and finish all assignments. Students are encouraged to relate the final project to on-going research. Details should be discussed with instructor.


Illustrative final projects include:

  • Development of new model and associated virtual experiments.

  • Validation of existing model and new virtual experiments.

  • Extensive virtual experimentations and theory building with existing model.

  • Docking (model-to-model comparison) of two or more existing models.

  • Extensive critique and meta-analysis of existing models possibly including new runs using said models.

  • Application of existing model to new area

  • Robustness analysis of statistical procedures using simulate data.

  • Development and testing of “dynamic measures” or “visualization procedures” for existing models.

  • Development and testing of “dynamic measures” or “visualization procedures” using simulated data..

  • Making two or more models inter-operable and demonstrating said inter-operability.

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