| Summary: | 碩士 === 國立中興大學 === 都市計劃研究所 === 84 === Because most of the research on planning behavior dose not
consider the spatial element, this thesis explorse effects of
information gathering in planning behavior on evolution of
spatial systems. Because the prisoner''s dilemma game
incorporates simple group interactions and can be used to
simulate land development behavior, the research uses that model
as decision rules in spatial interactions. However, the
prisoner''s dilemma game dose not consider the spatial element
either;therefore,the simulations conconducted in the research
are based on models of cellular automata which are spatial in
nature and have been thoroughly studied,in combination with the
prisoner''s dilemma game and planning elements for computer
simulations. From these simple spatial interaction models, we
can observe how information gathering affects structural
features of evolutions of these systems. It was found that
increasee in planning investmentresulted in cellular automata
more sensitive to the payoff parameter when the interactionrange
was small, while less sensitive when that range became large,
but the difference was not significant. Increase in planning
investment also resulted in more diverse, complex evolution
patterns. When the interaction range increased holding planning
investment constant,the range of the payoff parameter was
reduced and cellular automata were more sensitive to the payoff
parameter. That is, small changes in the payoff parameter led to
change in evolution rules. In addition, the C (cooperate)group
in whichagents were adjacent to each other was in a better
position than the D (defect)group to survive, implying that the
spatial element had effects on system evolutions and that when
applying economic decision models in spatial models, the
equilibrium of the prisoner''s dilemma game need be modified.
According to Wolfram''s classification, the cellular automata
resulting from the computer simulations falled into class one
and two, with only one exception belonging to class three or
four. Most results from the simulations evolved into stable,
predictable structures. When the range of cells'' values, The
interaction range, and planning investment increase,it is more
likely that cellular automata would fall into class four. This
implies that real world complex systems, such as cities, might
have inherently class four structures, which can serve as a
hypothesis for establishing urban growth models based on
cellular atuomata. Key words:planning behavior, infornation
gathing, prisoner''s dilemma,cellular automata,planning
investment.
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