3 research outputs found
An Evolutionary Game Theoretic Model of Rhino Horn Devaluation
Rhino populations are at a critical level due to the demand for rhino horn
and the subsequent poaching. Wildlife managers attempt to secure rhinos with
approaches to devalue the horn, the most common of which is dehorning. Game
theory has been used to examine the interaction of poachers and wildlife
managers where a manager can either `dehorn' their rhinos or leave the horn
attached and poachers may behave `selectively' or `indiscriminately'. The
approach described in this paper builds on this previous work and investigates
the interactions between the poachers. We build an evolutionary game theoretic
model and determine which strategy is preferred by a poacher in various
different populations of poachers. The purpose of this work is to discover
whether conditions which encourage the poachers to behave selectively exist,
that is, they only kill those rhinos with full horns.
The analytical results show that full devaluation of all rhinos will likely
lead to indiscriminate poaching. In turn it shows that devaluing of rhinos can
only be effective when implemented along with a strong disincentive framework.
This paper aims to contribute to the necessary research required for informed
discussion about the lively debate on legalising rhino horn trade
Understanding responses to environments for the Prisoner's Dilemma: A meta analysis, multidimensional optimisation and machine learning approach
This thesis investigates the behaviour that Iterated Prisonerās Dilemma strategies
should adopt as a response to diļ¬erent environments. The Iterated Prisonerās Dilemma
(IPD) is a particular topic of game theory that has attracted academic attention due
to its applications in the understanding of the balance between cooperation and com
petition in social and biological settings.
This thesis uses a variety of mathematical and computational ļ¬elds such as linear al
gebra, research software engineering, data mining, network theory, natural language
processing, data analysis, mathematical optimisation, resultant theory, markov mod
elling, agent based simulation, heuristics and machine learning.
The literature around the IPD has been exploring the performance of strategies in the
game for years. The results of this thesis contribute to the discussion of successful
performances using various novel approaches.
Initially, this thesis evaluates the performance of 195 strategies in 45,600 computer
tournaments. A large portion of the 195 strategies are drawn from the known and
named strategies in the IPD literature, including many previous tournament winners.
The 45,600 computer tournaments include tournament variations such as tournaments
with noise, probabilistic match length, and both noise and probabilistic match length.
This diversity of strategies and tournament types has resulted in the largest and most
diverse collection of computer tournaments in the ļ¬eld. The impact of features on
the performance of the 195 strategies is evaluated using modern machine learning and
statistical techniques. The results reinforce the idea that there are properties associated
with success, these are: be nice, be provocable and generous, be a little envious, be
clever, and adapt to the environment.
Secondly, this thesis explores well performed behaviour focused on a speciļ¬c set of IPD
strategies called memory-one, and speciļ¬cally a subset of them that are considered extortionate. These strategies have gained much attention in the research ļ¬eld and
have been acclaimed for their performance against single opponents. This thesis uses
mathematical modelling to explore the best responses to a collection of memory-one
strategies as a multidimensional non-linear optimisation problem, and the beneļ¬ts of
extortionate/manipulative behaviour. The results contribute to the discussion that
behaving in an extortionate way is not the optimal play in the IPD, and provide
evidence that memory-one strategies suļ¬er from their limited memory in multi agent
interactions and can be out performed by longer memory strategies.
Following this, the thesis investigates best response strategies in the form of static
sequences of moves. It introduces an evolutionary algorithm which can successfully
identify best response sequences, and uses a list of 192 opponents to generate a large
data set of best response sequences. This data set is then used to train a type of
recurrent neural network called the long short-term memory network, which have not
gained much attention in the literature. A number of long short-term memory networks
are trained to predict the actions of the best response sequences. The trained networks
are used to introduce a total of 24 new IPD strategies which were shown to successfully
win standard tournaments.
From this research the following conclusions are made: there is not a single best strategy
in the IPD for varying environments, however, there are properties associated with the
strategiesā success distinct to diļ¬erent environments. These properties reinforce and
contradict well established results. They include being nice, opening with cooperation,
being a little envious, being complex, adapting to the environment and using longer
memory when possible