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Andrii, Cherniak (2014) EXPLORING BEHAVIORAL PATTERNS IN COMPLEX ADAPTIVE SYSTEMS. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Many phenomenons in real world can be characterized as complex adaptive
systems (CAS). We are surrounded with a huge number of communicating and
interacting agents. Some of those agents may be capable of learning and
adapting to new situation, trying to achieve their goals. E-commerce,
social media, cloud computing, transportation network and real-time ride
sharing, supply chain are a few examples of CAS. These are the systems
which surround us in every day’s life, and naturally we want to make sense
of those systems and optimize systems’ behavior or optimize our behavior
around those systems. Given the complexity of these systems, we want to
find a set of simplified patterns out of the seeming chaos of interactions
in a CAS, and provide more manageable means of analysis for such systems.

In my thesis I consider a few example problems from different domains:
modeling human behavior during fire evacuation, detection of notable
transitions in data streams, modeling finite resource sharing on a
computational cluster with many clients, and predicting buyer behavior on
the marketplace. These (and other) seemingly different problems
demonstrate one important similarity: complex semi-repetitive or
semi-similar behavior. This semi-repetitive behavior poses a challenge to
model such processes. This challenge comes for two major reasons:
1 ) state-space explosion and sparsity of data
2 ) critical transitions and precision of process modeling

I show, that the analysis of smilingly different CAS coming from different
domains, can be performed by following the same recipe.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Andrii, Cherniakaic3@pitt.eduAIC3
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairZadorozhny, Vladimirvladimir@sis.pitt.eduVIZ
Committee MemberDruzdzel, Marek J.marek@sis.pitt.eduDRUZDZEL
Committee MemberMunro, PWM
Committee MemberPelechrinis, Konstantinoskpele@pitt.eduKPELE
Committee MemberBridgewater,
Date: 28 May 2014
Date Type: Publication
Defense Date: 22 April 2014
Approval Date: 28 May 2014
Submission Date: 6 May 2014
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 178
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: complex adaptive systems, multi-agent systems, NP-hard problems
Related URLs:
Date Deposited: 28 May 2014 18:53
Last Modified: 15 Nov 2016 14:20

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