M. Khoshnevisan, S. Bhattacharya, F. Smarandache
ARTIFICIAL INTELLIGENCE AND RESPONSIVE
Utility Index Function (Event Space D)
y = 24.777x2 - 29.831x + 9.1025
Expected excess equity
M. Khoshnevisan, S. Bhattacharya, F. Smarandache
ARTIFICIAL INTELLIGENCE AND RESPONSIVE
Dr. Mohammad Khoshnevisan, Griffith University, School of Accounting and Finance,
Sukanto Bhattacharya, School of Information Technology, Bond University, Australia.
Dr. Florentin Smarandache, Department of Mathematics, University of New Mexico,
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This book has been peer reviewed and recommended for publication by:
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Dr. Sabin Tabirca, University College Cork, Department of Computer Science and
Dr. W. B. Vasantha Kandasamy, Department of Mathematics, Indian Institute of
Technology, Madras, Chennai – 600 036, India.
The International Statistical Institute has cited this book in its "Short Book Reviews",
Vol. 23, No. 2, p. 35, August 2003, Kingston, Canada.
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University of New Mexico, Gallup, USA
The purpose of this book is to apply the Artificial Intelligence and control systems to
different real models.
In part 1, we have defined a fuzzy utility system, with different financial goals,
different levels of risk tolerance and different personal preferences, liquid assets, etc. A
fuzzy system (extendible to a neutrosophic system) has been designed for the evaluations
of the financial objectives. We have investigated the notion of fuzzy and neutrosophiness
with respect to time management of money.
In part 2, we have defined a computational model for a simple portfolio insurance
strategy using a protective put and computationally derive the investor’s governing utility
structures underlying such a strategy under alternative market scenarios. The Arrow-Pratt
measure of risk aversion has been used to determine how the investors react towards risk
under the different scenarios.
In Part 3, it is proposed an artificial classification scheme to isolate truly benign tumors
from those that initially start off as benign but subsequently show metastases. A non-
parametric artificial neural network methodology has been chosen because of the
analytical difficulties associated with extraction of closed-form stochastic-likelihood
parameters given the extremely complicated and possibly non-linear behavior of the state
variables we have postulated an in-depth analysis of the numerical output and model
findings and compare it to existing methods of tumor growth modeling and malignancy
In part 4, an alternative methodological approach has been proposed for quantifying
utility in terms of expected information content of the decision-maker’s choice set. It is
proposed an extension to the concept of utility by incorporating extrinsic utility; which is
defined as the utility derived from the element of choice afforded to the decision-maker.
This book has been designed for graduate students and researchers who are active in the
applications of Artificial Intelligence and Control Systems in modeling. In our future
research, we will address the unique aspects of Neutrosophic Logic in modeling and data
Fuzzy and Neutrosophic Systems and Time Allocation of Money
School of Accounting & Finance
Griffith University, Australia
School of Information Technology
Bond University, Australia
University of New Mexico - Gallup, USA
Each individual investor is different, with different financial goals, different levels of
risk tolerance and different personal preferences. From the point of view of investment
management, these characteristics are often defined as objectives and constraints.
Objectives can be the type of return being sought, while constraints include factors such
as time horizon, how liquid the investor is, any personal tax situation and how risk is
handled. It’s really a balancing act between risk and return with each investor having
unique requirements, as well as a unique financial outlook – essentially a constrained
utility maximization objective. To analyze how well a customer fits into a particular
investor class, one investment house has even designed a structured questionnaire with
about two-dozen questions that each has to be answered with values from 1 to 5. The
questions range from personal background (age, marital state, number of children, job
type, education type, etc.) to what the customer expects from an investment (capital
protection, tax shelter, liquid assets, etc.). A fuzzy logic system (extendible to a
neutrosophic logic system) has been designed for the evaluation of the answers to the
above questions. We have investigated the notion of fuzzy and neutrosophiness with
respect to funds allocation.
2000 MSC: 94D05, 03B52
In this paper we have designed our fuzzy system so that customers are classified to
belong to any one of the following three categories: 1
*Conservative and security-oriented (risk shy)
*Growth-oriented and dynamic (risk neutral)
*Chance-oriented and progressive (risk happy)
A neutrosophic system has three components – that’s why it may be considered as just a
generalization of a fuzzy system which has only two components.
Besides being useful for clients, investor classification has benefits for the professional
investment consultants as well. Most brokerage houses would value this information as it
gives them a way of targeting clients with a range of financial products more effectively -
including insurance, saving schemes, mutual funds, and so forth. Overall, many
responsible brokerage houses realize that if they provide an effective service that is
tailored to individual needs, in the long-term there is far more chance that they will retain
their clients no matter whether the market is up or down.
Yet, though it may be true that investors can be categorized according to a limited
number of types based on theories of personality already in the psychological profession's
armory, it must be said that these classification systems based on the Behavioral Sciences
are still very much in their infancy and they may still suffer from the problem of their
meanings being similar to other related typographies, as well as of greatly
oversimplifying the different investor behaviors. 2
(I.1) Exploring the implications of utility theory on investor classification.
In our present work, we have used the familiar framework of neo-classical utility theory
to try and devise a structured system for investor classification according to the utility
preferences of individual investors (and also possible re-ordering of such preferences).
The theory of consumer behavior in modern microeconomics is entirely founded on
observable utility preferences, rejecting hedonistic and introspective aspects of utility.
According to modern utility theory, utility is a representation of a set of mutually
consistent choices and not an explanation of a choice. The basic approach is to ask an
individual to reveal his or her personal utility preference and not to elicit any numerical
measure.  However, the projections of the consequences of the options that we face and
the subsequent choices that we make are shaped by our memories of past experiences –
that “mind’s eye sees the future through the light filtered by the past”. However, this
memory often tends to be rather selective.  An investor who allocates a large portion of
his or funds to the risky asset in period t-1 and makes a significant gain will perhaps be
induced to put an even larger portion of the available funds in the risky asset in period t.
So this investor may be said to have displayed a very weak risk-aversion attitude up to
period t, his or her actions being mainly determined by past happenings one-period back.
There are two interpretations of utility – normative and positive. Normative utility
contends that optimal decisions do not always reflect the best decisions, as maximization
of instant utility based on selective memory may not necessarily imply maximization of
total utility. This is true in many cases, especially in the areas of health economics and
social choice theory. However, since we will be applying utility theory to the very
specific area of funds allocation between risky and risk-less investments (and investor
classification based on such allocation), we will be concerned with positive utility, which
considers the optimal decisions as they are, and not as what they should be. We are
simply interested in using utility functions to classify an individual investor’s attitude
towards bearing risk at a given point of time. Given that the neo-classical utility
preference approach is an objective one, we feel it is definitely more amenable to formal
analysis for our purpose as compared to the philosophical conceptualizations of pure
hedonism if we can accept decision utility preferences generated by selective memory.
If u is a given utility function and w is the wealth coefficient, then we have E [u (w + k)]
= u [w + E (k) – p], that is, E [u (w + k)] = u (w - p), where k is the outcome of a risky
venture given by a known probability distribution whose expected value E (k) is zero.
Since the outcome of the risky venture is as likely to be positive as negative, we would be
willing to pay a small amount p, the risk premium, to avoid having to undertake the risky
venture. Expanding the utilities in Taylor series to second order on the left-hand side and
to first order on the right-hand side and subsequent algebraic simplification leads to the
general formula p = - (v/2) u’’(w)/u’ (w), where v = E (k2) is the variance of the possible
outcomes. This shows that approximate risk premium is proportional to the variance – a
notion that carries a similar implication in the mean-variance theorem of classical
portfolio theory. The quantity –u’’ (w)/u’ (w) is termed the absolute risk aversion.  The
nature of this absolute risk aversion depends on the form of a specific utility function. For
instance, for a logarithmic utility function, the absolute risk aversion is dependent on the
wealth coefficient w, such that it decreases with an increase in w. On the other hand, for
an exponential utility function, the absolute risk aversion becomes a constant equal to the
reciprocal of the risk premium.
(I.2) The neo-classical utility maximization approach.
In its simplest form, we may formally represent an individual investor’s utility
maximization goal as the following mathematical programming problem:
Maximize U = f (x, y)
Subject to x + y = 1,
x ≥ 0 and y is unrestricted in sign
Here x and y stand for the proportions of investable funds allocated by the investor to the
market portfolio and a risk-free asset. The last constraint is to ensure that the investor can
never borrow at the market rate to invest in the risk-free asset, as this is clearly unrealistic
- the market rate being obviously higher than the risk-free rate. However, an overtly
aggressive investor can borrow at the risk-free rate to invest in the market portfolio. In
investment parlance this is known as leverage. 
As in classical microeconomics, we may solve the above problem using the Lagrangian
multiplier technique. The transformed Lagrangian function is as follows:
Z = f (x, y) + λ (1-x-y) … (i)
By the first order (necessary) condition of maximization we derive the following system
of linear algebraic equations:
Z = f - λ = 0 (1)
Z = f - λ = 0 (2)
Zλ = 1 - x - y = 0 (3) … (ii)
The investor’s equilibrium is then obtained as the condition f = f = λ*. λ* may be
conventionally interpreted as the marginal utility of money (i.e. the investable funds at the
disposal of the individual investor) when the investor’s utility is maximized. 
The individual investor’s indifference curve will be obtained as the locus of all
combinations of x and y that will yield a constant level of utility. Mathematically stated,
this simply boils down to the following total differential:
dU = f dx +f dy = 0 … (iv)
The immediate implication of (3) is that dy/dx = -f /f , i.e. assuming (f , f ) > 0; this gives
the negative slope of the individual investor’s indifference curve and may be equivalently
interpreted as the marginal rate of substitution of allocable funds between the market
portfolio and the risk-free asset.
A second order (sufficient) condition for maximization of investor utility may be also
derived on a similar line as that in economic theory of consumer behavior, using the sign
of the bordered Hessian determinant, which is given as follows:
|H| = 2β β f – β 2f – β 2f … (v)
βx and βy stand for the coefficients of x and y in the constraint equation. In this case we
have βx = βy = 1. Equation (4) therefore reduces to:
|H| = 2f – f – f … (vi)
If |H| > 0 then the stationary value of the utility function U* attains its maximum.