**Subject: **Re: fuzzy finance

**From: **Pramit Sarma (*pramits@bom8.vsnl.net.in*)

**Date: **Sat Mar 04 2000 - 07:02:23 MET

**sorted by:**[ date ] [ thread ] [ subject ] [ author ]**Next message:**Pramit Sarma: "Re: kosko's FAM"**Previous message:**Paul Victor Birke: "Re: fuzzy finance"**In reply to:**Paul Victor Birke: "Re: fuzzy finance"**Next in thread:**Paul Victor Birke: "Re: fuzzy finance"**Reply:**Pramit Sarma: "Re: fuzzy finance"

All,

I too have been watching this parley with some interest. Besides

interest in things AI, real-world stuff (process engineering) definitely

indicates the "steal with pride" thing. The way engineers put it, is

that which works well (demonstrably) shall be put to work!

It is, intuitively, clear that all this Theory of Possibility vs.

Probability is more like one multi-wrench vs. another. The possible

(probable?) problem being here, that here the two can and do have some

similar "heads". Systems common sense, rather than wrangling, seems to

indicate both tools are not only useful, the similarities need to

highlighted, leading to a clearer way of looking at the critical and

*useful* differences. Synergy as the key, and all that. That, and the

pretty clear systems axiom: "There is no Universal Model"!

It is possible that everyone in the discussion is not also involved in

fuzzy logic control (FLC), or rather in control systems. So, despite

being a strong proponent of FLC, nevertheless there are many quasi-crisp

controller models which have been working for decades, very well. One of

this set is the Optimal/Stochastic Controllers and Estimators (LQG,

Kalman, etc.)**. They are all variously based on probabilistic Gaussian

noise models. So indeed there are, and several, probabilistic control

applications. The fact that some of the most successful FLC fuzzy terms

tend to be Gaussian (an observation of my own FLC work, too) ... appears

to me as one of those synergies.

** (For an interesting debate on crisp/optimal control vs. fuzzy

control, with Athans vs. Zadeh, have a look at the IEEE Control Systems

Mag., June 1999)

Finally, I think that in systems the phrase "The end justifies the

means" is particularly attractive and devoid of most of its negative

connotations, for (intelligent) systems engineers ... and to the most

important person at the end of the loop: the end-user.

Pramit

Paul Victor Birke wrote:

*>
*

*> "Philip E. Bennett" wrote:
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*> >
*

*> > Paul, et al
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*> >
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*> > I've been monitoring this thread with muted interest. The discussion has
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*> > bounced around and touched on many topics but the main interest seems to be
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*> > in fuzzy vs probabilistic methods and their application to financial
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*> > modelling/trading.
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*> >
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*> > As a private trader, former institutional derivatives trader, and a bit of a
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*> > financial engineer I am concerned only with how well these techniques work
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*> > to help me create workable models. I define a workable model has one that
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*> > provides information so as to reduce the uncertainty regarding the
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*> > interpration of a specific situation or the course of action to take. In
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*> > this regard, IMHO, possibilistic (i.e. fuzzy logic) and probabilistic (i.e.
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*> > neural nets, datamining, etc.) methods are complementary and HIGHLY
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*> > synergistic! The use of GA's and SOM's to help fine tune the application of
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*> > these methods are also benefical.
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*> >
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*> > Would it be appropriate to suggest that if you're interested in gaining more
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*> > success in modelling you focus more on what can be gained from by using the
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*> > methods together?
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*> **********************
*

*> Dear Philip
*

*>
*

*> Well in fact that is what I had done at the time back in about 1996.
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*> While I appear to be a bit one sided in fact I am not so.
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*>
*

*> I would agree with you fully here. There a loads of insights to be
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*> obtain be sticking to formal Probability Theory and its offshoots. The
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*> University of Guelph library has a number of books on subjective
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*> probability assessment of uncertain prediction that derived in part from
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*> the Cold War as just a for instance. I was most interested modelling
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*> uncertainty in the marketplace. I became interested in the idea of
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*> using or borrowing from Fuzzy Set Theory the UltraFuzzy Function. You
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*> can roam around the UltraFuzzy using a Geometric Mean definition between
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*> the lower and upper bounds. These bounds can also be interpreted as
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*> probability limits.
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*>
*

*> So as an engineer Steal With Pride anywhere I can get ideas to obtain
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*> some advantage. I am not stuck in the ground fuzzy person. I try to
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*> use each where advantage.
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*>
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*> In discussing this problem at work today I was reminded that Fuzzy has
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*> shown advantage in Control Applications wherein a loop or control loop
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*> is the key idea here. I will pass this along to Peter Hamer in a few
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*> minutes. Probability Theory seems lacking in the control context. And
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*> maybe why is failed should be highlighted.
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*>
*

*> all for now,
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*>
*

*> Paul
*

*>
*

*> ############################################################################
*

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--========================================================================== Pramit 'Jake' Sarma

E-mail: PramitS@vsnl.com [Res] PSarma@ChE.IITB.ernet.in [Lab] W-mail: pramit_s@india.com

Processes + Control, Intelligent & Simulation Systems | P R O C I S S | {Systems + Mathematics}---{Real World Processes} ==========================================================================

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