vi, 48 p. : ill. A print copy of this thesis is available through the UO Libraries. Search the library catalog for the location and call number. / We compare the relaxation times of two random walks - the simple random walk and the metropolis walk - on an arbitrary finite multigraph G. We apply this result to the random graph with n vertices, where each edge is included with probability p = [Special characters omitted.] where λ > 1 is a constant and also to the Newman-Watts small world model. We give a bound for the reconstruction problem for general trees and general 2 × 2 matrices in terms of the branching number of the tree and some function of the matrix. Specifically, if the transition probabilities between the two states in the state space are a and b , we show that we do not have reconstruction if Br( T ) [straight theta] < 1, where [Special characters omitted.] and Br( T ) is the branching number of the tree in question. This bound agrees with a result obtained by Martin for regular trees and is obtained by more elementary methods. We prove an inequality closely related to this problem. / Committee in charge: David Levin, Chairperson, Mathematics; Christopher Sinclair, Member, Mathematics; Marcin Bownik, Member, Mathematics; Hao Wang, Member, Mathematics; Van Kolpin, Outside Member, Economics

Computing with mobile agents is rapidly becoming a topic of mainstream research in the theory of distributed computing. The main research questions undertaken in this study concern the feasibility of solving fundamental tasks in an anonymous network, subject to limitations on the resources available to the agent. The considered challenges include: exploring a graph by means of an agent with limited memory, discovery of the network topology, and attempting to meet with another agent in another network (rendezvous). The constraints imposed on the agent include the number of moves which the agent is allowed to perform in the network, the amount of state memory available to the agent, the ability of the agent to communicate with other agents, as well as its a priori knowledge of the network topology or of global parameters.