dc.contributor.author | Omwenga, VO | |
dc.contributor.author | Singh, CB | |
dc.contributor.author | Manene, MM | |
dc.contributor.author | Pokhariyal, GP | |
dc.date.accessioned | 2016-06-08T09:26:29Z | |
dc.date.available | 2016-06-08T09:26:29Z | |
dc.date.issued | 2015 | |
dc.identifier.citation | J. Meteorol. Rel. Sci., 7:3, 2015 | en_US |
dc.identifier.uri | http://www.kms.or.ke/images/stories/jmrsv7p3.pdf | |
dc.identifier.uri | http://hdl.handle.net/11295/96084 | |
dc.description.abstract | Environmental c
onflicts
arise as a consequence of
actions prevent
ing
or compel
ling
some outcome at
the resistance
to the actions.
More specifically, they are caused by anthropogenic activities tha
t strain
and damage the environment.
Modelling environmental conflict is one of the fundamental ways
providing means of solving them.
In order to understand and model
them
, it is
critical
to identify
potential
and/
or existing conflict causes
(structural ca
uses or proximate causes)
, as well as possible
factors contributing to peace.
In this paper, the dynamic time varying model for predicting
environmental conflict is developed
using
Bayesian theory
. The initial (state) conditions which play a
significant r
ole in the success of conflict resolution are estimated through a logistic probability model.
An analogy on the application of the model in
modelling
of environmentally
-
induced conflict is
given | en_US |
dc.language.iso | en | en_US |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | * |
dc.subject | Bayesian rule | en_US |
dc.subject | Environmental | en_US |
dc.subject | Conflic Dynamic state | en_US |
dc.subject | Initial conditions | en_US |
dc.subject | Logistic probability model | en_US |
dc.subject | Ultimatum game | en_US |
dc.title | Conflict Prediction Model in a Dynamic State | en_US |
dc.type | Article | en_US |