299 research outputs found
Relative rationality: Is machine rationality subjective?
Rational decision making in its linguistic description means making logical
decisions. In essence, a rational agent optimally processes all relevant
information to achieve its goal. Rationality has two elements and these are the
use of relevant information and the efficient processing of such information.
In reality, relevant information is incomplete, imperfect and the processing
engine, which is a brain for humans, is suboptimal. Humans are risk averse
rather than utility maximizers. In the real world, problems are predominantly
non-convex and this makes the idea of rational decision-making fundamentally
unachievable and Herbert Simon called this bounded rationality. There is a
trade-off between the amount of information used for decision-making and the
complexity of the decision model used. This explores whether machine
rationality is subjective and concludes that indeed it is
Impact of Artificial Intelligence on Economic Theory
Artificial intelligence has impacted many aspects of human life. This paper
studies the impact of artificial intelligence on economic theory. In particular
we study the impact of artificial intelligence on the theory of bounded
rationality, efficient market hypothesis and prospect theory
On Robot Revolution and Taxation
Advances in artificial intelligence are resulting in the rapid automation of
the work force. The tools that are used to automate are called robots. Bill
Gates proposed that in order to deal with the problem of the loss of jobs and
reduction of the tax revenue we ought to tax the robots. The problem with
taxing the robots is that it is not easy to know what a robot is. This article
studies the definition of a robot and the implication of advances in robotics
on taxation. It is evident from this article that it is a difficult task to
establish what a robot is and what is not a robot. It concludes that taxing
robots is the same as increasing corporate tax
Bayesian Approach to Neuro-Rough Models
This paper proposes a neuro-rough model based on multi-layered perceptron and
rough set. The neuro-rough model is then tested on modelling the risk of HIV
from demographic data. The model is formulated using Bayesian framework and
trained using Monte Carlo method and Metropolis criterion. When the model was
tested to estimate the risk of HIV infection given the demographic data it was
found to give the accuracy of 62%. The proposed model is able to combine the
accuracy of the Bayesian MLP model and the transparency of Bayesian rough set
model.Comment: 24 pages, 5 figures, 1 tabl
Evaluating the Impact of Missing Data Imputation through the use of the Random Forest Algorithm
This paper presents an impact assessment for the imputation of missing data.
The data set used is HIV Seroprevalence data from an antenatal clinic study
survey performed in 2001. Data imputation is performed through five methods:
Random Forests, Autoassociative Neural Networks with Genetic Algorithms,
Autoassociative Neuro-Fuzzy configurations, and two Random Forest and Neural
Network based hybrids. Results indicate that Random Forests are superior in
imputing missing data in terms both of accuracy and of computation time, with
accuracy increases of up to 32% on average for certain variables when compared
with autoassociative networks. While the hybrid systems have significant
promise, they are hindered by their Neural Network components. The imputed data
is used to test for impact in three ways: through statistical analysis, HIV
status classification and through probability prediction with Logistic
Regression. Results indicate that these methods are fairly immune to imputed
data, and that the impact is not highly significant, with linear correlations
of 96% between HIV probability prediction and a set of two imputed variables
using the logistic regression analysis
Bayesian approach to rough set
This paper proposes an approach to training rough set models using Bayesian
framework trained using Markov Chain Monte Carlo (MCMC) method. The prior
probabilities are constructed from the prior knowledge that good rough set
models have fewer rules. Markov Chain Monte Carlo sampling is conducted through
sampling in the rough set granule space and Metropolis algorithm is used as an
acceptance criteria. The proposed method is tested to estimate the risk of HIV
given demographic data. The results obtained shows that the proposed approach
is able to achieve an average accuracy of 58% with the accuracy varying up to
66%. In addition the Bayesian rough set give the probabilities of the estimated
HIV status as well as the linguistic rules describing how the demographic
parameters drive the risk of HIV.Comment: 20 pages, 3 figure
Blockchain and Artificial Intelligence
It is undeniable that artificial intelligence (AI) and blockchain concepts
are spreading at a phenomenal rate. Both technologies have distinct degree of
technological complexity and multi-dimensional business implications. However,
a common misunderstanding about blockchain concept, in particular, is that
blockchain is decentralized and is not controlled by anyone. But the underlying
development of a blockchain system is still attributed to a cluster of core
developers. Take smart contract as an example, it is essentially a collection
of codes (or functions) and data (or states) that are programmed and deployed
on a blockchain (say, Ethereum) by different human programmers. It is thus,
unfortunately, less likely to be free of loopholes and flaws. In this article,
through a brief overview about how artificial intelligence could be used to
deliver bug-free smart contract so as to achieve the goal of blockchain 2.0, we
to emphasize that the blockchain implementation can be assisted or enhanced via
various AI techniques. The alliance of AI and blockchain is expected to create
numerous possibilities
Creativity and Artificial Intelligence: A Digital Art Perspective
This paper describes the application of artificial intelligence to the
creation of digital art. AI is a computational paradigm that codifies
intelligence into machines. There are generally three types of artificial
intelligence and these are machine learning, evolutionary programming and soft
computing. Machine learning is the statistical approach to building intelligent
systems. Evolutionary programming is the use of natural evolutionary systems to
design intelligent machines. Some of the evolutionary programming systems
include genetic algorithm which is inspired by the principles of evolution and
swarm optimization which is inspired by the swarming of birds, fish, ants etc.
Soft computing includes techniques such as agent based modelling and fuzzy
logic. Opportunities on the applications of these to digital art are explored.Comment: 5 page
A note on the separability index
In discriminating between objects from different classes, the more separable
these classes are the less computationally expensive and complex a classifier
can be used. One thus seeks a measure that can quickly capture this
separability concept between classes whilst having an intuitive interpretation
on what it is quantifying. A previously proposed separability measure, the
separability index (SI) has been shown to intuitively capture the class
separability property very well. This short note highlights the limitations of
this measure and proposes a slight variation to it by combining it with another
form of separability measure that captures a quantity not covered by the
Separability Index
Comparison of Data Imputation Techniques and their Impact
Missing and incomplete information in surveys or databases can be imputed
using different statistical and soft-computing techniques. This paper
comprehensively compares auto-associative neural networks (NN), neuro-fuzzy
(NF) systems and the hybrid combinations the above methods with hot-deck
imputation. The tests are conducted on an eight category antenatal survey and
also under principal component analysis (PCA) conditions. The neural network
outperforms the neuro-fuzzy system for all tests by an average of 5.8%, while
the hybrid method is on average 15.9% more accurate yet 50% less
computationally efficient than the NN or NF systems acting alone. The global
impact assessment of the imputed data is performed by several statistical
tests. It is found that although the imputed accuracy is high, the global
effect of the imputed data causes the PCA inter-relationships between the
dataset to become altered. The standard deviation of the imputed dataset is on
average 36.7% lower than the actual dataset which may cause an incorrect
interpretation of the results.Comment: 7 page
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