267 research outputs found
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
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
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
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
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
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
Artificial Intelligence Techniques for Steam Generator Modelling
This paper investigates the use of different Artificial Intelligence methods
to predict the values of several continuous variables from a Steam Generator.
The objective was to determine how the different artificial intelligence
methods performed in making predictions on the given dataset. The artificial
intelligence methods evaluated were Neural Networks, Support Vector Machines,
and Adaptive Neuro-Fuzzy Inference Systems. The types of neural networks
investigated were Multi-Layer Perceptions, and Radial Basis Function. Bayesian
and committee techniques were applied to these neural networks. Each of the AI
methods considered was simulated in Matlab. The results of the simulations
showed that all the AI methods were capable of predicting the Steam Generator
data reasonably accurately. However, the Adaptive Neuro-Fuzzy Inference system
out performed the other methods in terms of accuracy and ease of
implementation, while still achieving a fast execution time as well as a
reasonable training time.Comment: 23 page
Artificial Intelligence and Asymmetric Information Theory
When human agents come together to make decisions, it is often the case that
one human agent has more information than the other. This phenomenon is called
information asymmetry and this distorts the market. Often if one human agent
intends to manipulate a decision in its favor the human agent can signal wrong
or right information. Alternatively, one human agent can screen for information
to reduce the impact of asymmetric information on decisions. With the advent of
artificial intelligence, signaling and screening have been made easier. This
paper studies the impact of artificial intelligence on the theory of asymmetric
information. It is surmised that artificial intelligent agents reduce the
degree of information asymmetry and thus the market where these agents are
deployed become more efficient. It is also postulated that the more artificial
intelligent agents there are deployed in the market the less is the volume of
trades in the market. This is because for many trades to happen the asymmetry
of information on goods and services to be traded should exist, creating a
sense of arbitrage
Modeling and Controlling Interstate Conflict
Bayesian neural networks were used to model the relationship between input
parameters, Democracy, Allies, Contingency, Distance, Capability, Dependency
and Major Power, and the output parameter which is either peace or conflict.
The automatic relevance determination was used to rank the importance of input
variables. Control theory approach was used to identify input variables that
would give a peaceful outcome. It was found that using all four controllable
variables Democracy, Allies, Capability and Dependency; or using only
Dependency or only Capabilities avoids all the predicted conflicts.Comment: 6 page
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