Acta Polytechnica Vol. 43 No. 112003

Management Processes in
Technical-economic Designo
Decision-making, Fractals and Market
Bubbles
V. Beran

Keywords: dccision making, nta,nagenxent, cellular automnn, fractak, market hubbles, technical economic design, simulati.ons, process and
regulation, steering process and mnnagernent.

I Introduction

Technical-economic processes are generally events run-
ning in t'imc and spam (factual space of proposal opportu-
nities). Most of these events are related to their economic

W-qdt and their technical-economic structure. In their clas-

sical analytical form, models of technical-economic events are
based on a description of systems of equations, matrix mod-
els, or models based on quantitative formulas and use of
differential or difference calculus if qualitative descriptions
are needed. There are also other models, based on graph the-
ory (Demel, 2002), symbolical logic and sets (\y'lcek, Beran,
1984) and also on cellular automats (Wolfiam, 2002), or sym-
bolic verbal oriented models.

Existing models operate mostly on the basis of analytic
concepts of description, quantitntiae or selected ry,alintiae
relations. However most of them do not contain built-in
dzcisian-making mechanisms or connections to possible opara-

tional managunmt or steering interuentiuts that can be used in
a model that enables design. A large range of models are
isolated fiom the concept where a model may be a subject of
experiments lik chnnges of parameters, or more likely the
adjustment of parameter structure. Let us use other terminol-
ogy. Models used in practice restrict their own possible goals
and describe almost exclusively already existing (known) real-
ity. The approach resembles a static calculation that has to
prove the stability of an already existing structure (scheme). It
is worth dealing with this approach, while very often afnished

lready) fusign of a technical product or proposal has to be
proved or made eflicient (ad post) by an economic calculation.
Commonly createdposl dcsign calculations like total construc-
tion costs [fCC), life-cycle cost (LCC), revenue per year
(ROI), feasible construction time or cash flow schedule, di-
verse deadlines, or other quantifiable parameters have to
appreciate existing design parameters, no matter how suit-
able the design might be if there were methods influenced by
the conceptual starting position. These situations may be
called technical-economic dzsign ex post. The economics of
such situations is a procedure that ffirms b:ut does not create.

Design, howeveq involves creating new possibilities and new

spaces, or functions. The economics of design of new techni-

46

cal solutions should solve and anticipate. It should be aimed
at possible future solutions and this is activity ex anle (Beran V,
2002) Modern design has to create values to which Homo
economil:us is willing to award a purchase price. This is not a

question of modern trends but of the ability to create ailcd
aalue. Arry reproduced and repeated solutions that are avail-
able in many variations and are a matter of mass production
have only a decreasing ability to create some ad.ded ualue and
in its ultimate implication, profit.

On the other hand Homo technic'us generally aims to pres-
ent his technical brilliance and skills. A result that might be
called an optirutl depends for most projects on a wide range of
asserted fucisions in time and technical-economic space (Bayes,

1763). Each particular decision (and sequence of decisions)
should be chosen optimally. This is a so-called necessary con-
dition of optimality in the time ongoing process.

Applied mathematics offers its own ways in terms of
model ranking (e.g. lineaq non-linea4 static, dynamic, etc.).
Economics and organization of production processes uses its
own instruments. Graph theory mathematical models from
elementary linear models to complicated dynamical behav-
iour of nonlinear economic processes (with cycles of balance
and deterministic chaotic states, (Kubik, 1982)) offer interest-
ing applications in economics.

Engineers and economists - using simple modelling tools
like timetables, budgeting, costing, and enterprise financial
and management planning - should know, however; how to
incorporate the tools they are using into the hierarchy of
sophistication, utility and benefits. The field of economic
and social sciences certainly offers a high number of views

and impulses. The services provided by industrial economics
(microeconomics) depend on a range of complicated instru-
ments. Util,ity aaLues are transformed by means of organization
and economic models into the consumption. If the model is

well designed these transformations are positive. However,
badly designed model may lose many opportunities and
values, waste resources and effort.

A good model of reality is our goal, an apposite, useful
reality model. The right model leads to represents a true
picture about functions, opportunities and, designed addnd

aalues. We are in point of fact seeking for models that



Acta Polytechnica Vol. 43 No. l/2003

represent and refer to those qualities that have can create
innovative design, create an added value over a technical-eco-
nomic common (standard) design and solutions. Economics,
unlike technical science, presents values that are partly imper-
manent (unsustainable). Values change in time, vanish and
others come into view. Economics requires a description of
universal atffibutes on the one hand and changes its involve-
ments and objectives from one time to another. In its nature
it is the technical piece of work that creates the long-term
statnirnble values of the economic life-cycle of every region,
enterprise and city.

For technical, managerial or technological reasoning of
economic issues, a comprehensive definition of the compo-
nents (processes, etc.) is needed, followed by the ability to
define theses on the basis of a model. The whole range of
economic problems defines its processes (elements) simulta-
neously with a solution method of the given task. In economic
applications, the solution and the chosen definition of the
problem generate a balanced facet. Suitable illustrative exam-
ples of the above mentioned situation were and still are
many diflerent applications of production scheduling, time
scheduling models like CPM, MPM, RAMPS, dynamic time
schedule, etc. The same situation also exists in other techni-
cal-economic disciplines that use quantitative methods, the-
ory of stores, theory of renewal, structural analyses, theory
of decision-making, etc. A definition of elemental syntactic
process - components is just the starting point of each new
task. HoweveL every single application that is born in techni-
cal-economic disciplines is endangered by an incorrect or
incomplete definition of the elemenlzl components or pro-
cesses. In a broad range of applications we realize that a
number of attributes of content quantities and content quan-
tities themselves have to be changed. A whole range of tasks
fail to create the sort of internal boundaries that would
prevent solutions of the task or solutions that lead in an
unsustainable direction. Indeed these above mentioned risks
are generally valid. A technical-economic task rarely rvorks
well on the basis of a purely physical form. The economic
effect may be proved only with difliculty by means of other
controlling models. The proof usually runs on the basis of
expert judgments. Mistakes that are dragged in often diffr-
cult to detect, and lve are looking for sustainable long-lived
solutions.

Under conditions for sustainability it is desirable to define
not only sets of activities (sets of processes) that operate as

substance (material) transforming controlled modek (P), and
also comprehensive (derivative) structures of a controlling
character; i.e. a set of controlling rnodek (steering rnodels) (L).

Let us delineate the synergetic symbiosis of P and L as a

process of management (M), (in Vliek, J., Beran V,1984, Beran,
1997 and 1999). To simplify the situation, the operating
model will be described only in the space of quantitative de-
rived components created on the basis of so called networked
processes P,+ N, <A, K>, where A represents a set of compo-
nents with their physical descriptions U, dependences in time

D and a set of dependences of quantitative character Q. K is
an interconnection set (causality) between components with
their set of physical descriptions V, construction of connec-

tions A, and starter ofconnections e.

In symbolic form, a notation of milm,gemenf appears as:

(l)

til 
" 

:L(*frn glo )l.l, r =(A,K)

MN=

A = (U,D,e)
K= (V, A, e)

L=(.)

-=(u-,o-,'-)
o- = (t-, oim (rr))

An operating process on the level ofnenvorking process
M,N is executed only if we put aside and separately define
dccision making proceduras for selection ofthe variant and alter-
native solutions of possible operating (steering) management
interventions created by means of <p (f, P, L), selected by
decision-making mechanisrnsD-. Deciding is necessary for ex-
ecution of targets. Without it an operational leading process
might only use regulative rules for decision-making proce-
dures D' inside L,., provided that the operational leading
(steering) process was created on a sufliciently effrcient level,
i.e. it would be on the level L. (so-called planned level). A
lower elementary level Lo and causal leading process L*
(on the basis of casual relations) does not offer any chance
to prevent the operational process from transitioning to de-
generative states. The network shaped (designed) process is
quite arduous for reasons of scheduling (steering). It requires
that the steering (leading) process will substitute (virtually
describe) the elements of reality ( P - process), will be able to
influence the structure and at the same time to control the
model of regulative decision part D'. The decision compo-
nent of control block D- is part of the management model
and in its frame was also designed for this function. We

suppose that it is stable in the developed scheme (l)and that
a component of information transmission K- is able to imple-
ment in time all necessary communication about managerial
information and interventions. K- has to secure the transfer
of all necessary information for the needs of the steering
model L,.,.

Let us attempt to reply briefly to some questions concern-
ing to entry (l) from the time-oriented viewpoint on decision
descriptor D-. It would certainly be interesting to know
whether, in designing a technical work, the fuc'ision processes

have certain specific propertfus in their use. It is important
to comprehend this, especially when we are looking for expla-
nations and causes for an unexpected project development.
The number of successful or unsuccessful outcomes of engi-
neering projects from the hands of Homo economians or
technicus depends on the presence or absence of a rational
vision. The very character of steering space, its structure in
time, may be so specific for applications in engineering de-

sign non-homogeneous that unexpected disturbance of the
technical worh susbinabilitl life-cycle occurs. The sequence of de-

cision-making in steering processes (i.e. management) may
have other lrles and places for implementation than have
usually been envisaged until now. The reasons are usually
economic limits and indicators; however:, their duration is
mainly only a fragment of the total technical life-rycle of a
designed project. Application of designing might have pro-
ceeded differently if better rules had been applied for selec-

tion of their solutions (decisions) inside a steering process (L),

at least better in terms that we use to thing about these D -
rules. This is the main reason why this article was written.

AN



Acta Polytechnica Vol. 43 No. ll2001

Let us pose several fundamental questions:

L Is the steering area for decision-making homogeneous?
2. Does non-homogeneous decision-making implementa-

tion of an environment (space, area) influence the deci-
sion steering process D-?

3. To what extent is decision process D- in time srrata t,
affected by the previous decisions in the former time
layer?

4. To what extent will be decision processes D- in time
strata tx+,? (further future) affect decisions in a time nearer
to layer fr.

Before we try to reply to this cluster of questions, it is desir-
able to elucidate the importance and utility of such search of
an$^r'ers. Horno econont;btn or technbus for many generations
worked on technical solutions and judged them mainly
intuitively.

Nowadays the entire contrivance of public competitions
in the EU, the USA and many other countries is quite con-
sciously based more or less on mechanisms that assume a
homogeneous decision area. The basis of Bayes probability
theory (Bayes,1763) was extensively applied for technical-
-economic decision-making in the second half of the 20d
century (Raiffa, Schleifer l96l).

Consciously searching for new productive techniques to
protect decisions from unfavourable judgements, working
with enhanced data or facts, we may be critical of the existing
methodological situation. The course and usage of methods
practiced for technical-economic project design will neces-
sarily change. On the basis of knowledge of the model a

technician and an economist uses or can freely create steering
rneasures (interventions) in the sense of q(1 B L) in (l). More
sophisticated methods are under development (simulation,
goal parameterisation, optimisation, construction of scenar-
ios, etc.).

For the purposes of furrher explanation it will not be
necessary to distinguish among particular phases or sophisti-
cation in the description of a real process (P) or steering
processes (L). Completing ro a model that is able to generate
interventions carrying (propagating) changes from the sreer-
ing level to the realisation level we have marked in the
previous sentence as M. Tools for formation of steering inter-
ference g(1 B L) may, without requirements for determining
of their robustness or suitability, are illustrated as follows:

o search for satisfaction of goak by means of solutions regard-
less of restrictive conditions, we will use rhe symbol goal
conditioned by process paramerers, see (la),

o the search for solutions on the basis of simulations of
chosen parameters in determined boundaries, will be
written as simul (lb),

o the search for solutions by virtue ofthe factual conception
of the future solution along separate stage scenarios, mark-
ing decant SG€tr1, Sc€t12, ... (see (lc)),

. the search for an optimal solution with reference to setting
restrictive conditions is marked optim (see (ld)),

Therefore, we cannot speak about a single management
model class. A whole range of approaches exists, which differ
in evidence and efficiency.

Among the first class modelwewill rank models changingthe goal objectiue value of process P by means of selected parameter
xi,

(la)

The second model rypes for creating steering intervention for management are models based on simulations and
parameterisation of calculations of changes in particular input values. Simulation is universally directed at the behaviour pat-
tern fiom the standpoints of changing parameters that describe process P

Mgoal oriented ={n1", 
(xt' xz' "' xn)}

Msimulation or steerins inrerventions = 
{ ",1r, | ( ",* t';,";,I|1",4, ) }

(lb)

The third class of model description engages models with scenarios as a starting point for calculations of steering interven-
tions. Steering scenarios will be understood as a sequence developed in time and space, realized on the basis of a process fl and
will be written down as

( lc)

The fourth model class will be models using optimisation as a supporting technique. By virtue of optimisation function g (.)
optimal arg'uments x" are searched. Accomplishment of steering management intewention restricted on process P may be
written down as

(4,x2,...,xn)
(a -+ xi,b - , i,..)

)

SCOII2

(x1,x2,"',x) I
(a --> xi,b - * j,..)l

){
)

-+xi d-*j::
-) xi,f -) x j,.

(:
(e

(e -> xi,f - x j,..) optim_

48

SGen3

(ld)



Acta Polytechnica Vol. 43 No. ll200\

Practical modelling of steering intervenrion for manage_
menr has not a uniform character and does not rely on a sin_
gle theoretical or practical interim theory.

The basis of any management model remains process.
A description of the model can rake various forms. An ideal
pedagogical form is description by means ofa system ofequa-
tions (linea6 nonJinea4 ordinary differential equarions, etc.)
that. are well-known in classical mathematics. In economic
practice there are many situations where a description of rele-
vant processes is currently solved by means of data and calcu-
lation built-up, for example, into databases. In most cases, ir
from the mathematical viewpoint this involves a small system
of linear equations. Recording these in mathematical nota-
tion, howeve6 would be too rigid to provide evidence for
exact interpretation is limited from the economic point of
view. Links between COMPUTER-AIDED DESIGN and CAM
are currently made by relating a chart (drawing) and related
calculations (most often for economic reasons), which de-
scribe sophisticated processes, and these are not immediately
presented as a classical mathematic approach.

This paper is an attempr at generalization. The author
considers as a process any technical-economic presentation
of reality, and he considers as steering mod.els 

'any 
abstract

description of the reality of processes that is employable for
elaboratingmnnagement interuentions of these processes. In this
sense there is certainly one management trend that exploits
modelling as an instrumenr for generating steering propos-
als. Assumption of homogeneous application fields providing
production resources and application of continuous space
and time can be very limiting, if not misguiding.

The decision process D = (F, dim(h))mentioned in notar-
ion (l), used in processes P (suitable for be evaluation ofreal
situations), o.D = (F-, Oim(n))in the course of L applications

for steering processes requires completion of the relevant area
homogeneity within which the solution, creared from <p(t, P,

L), can be implemented.
The impression that is feasible to break away fi'om the

existing mathematical theoretical rudiments will not however
be correct. Even in modelling of P and L, the elaborated
model potential does not represent substitutable man-years
exertion. However:, implementation and propagation of de-
cisions (implementation of decision interventions) may be
done more carefully, ifwe are aware of the complicated appli-
cation area.

2 Decisions on the basis of D*-" and
shaped decision space
In the sense of record (l) there is the real manufacturinp;

or investment process P, which capitalizes on an existing

market and produces volume parameter Q (here filling the
a defined market area with products). So the investments
intended to make use of demand in an economic area, for
example, dwellings in time periods t=1,2, ... and separate
areasA, B, C,... .

Existing demand for goods in time to is filled by volumes
of initial investment houses and lands inA, B, C, .. . However,
the filling will be limited by the available resources (capital).
For simpliciry we can suppose that the market space can from

the voluminous point of view (Q) be filled and have the value
l, or be unfilled and have the value 0. We can write that the
management decision will be Aq= l, or Aa= 0. In this situation
we can write the vector of the starting action state for p,_o (A,
B, C, ...) as vector (1, l, l, l, 1, l, 1,...), when denoting the
fully fiLled-out space of potentinl needs. The vector initialising
state processes Pr=o= (0, 0, 0, 0, 0, 0, 0,...) expresses an erypt)
space ofpossible needs (for example of housing in the initial time
period and sites A, B, C,.... Supposing that the steering
process is sketched in such a way that it evaluates a positive
situation of the state at the given rime period land determin-
es realization of new capital assets there, where segment area
A, B, C,... has been reordered (sorted) into the import-
ance-reflecting chain for a decision-maker according to his
investment interests, for the pasr time period f - I in the
newly ordered areas M, N, O (reveal M as neighbouring left, N
is the position of the decision maker, O neighbouring right).

The past might acquire values and be written as

tr-, = ( , Ma =t, tta =# .oa = L...),
or more concisely as (2)

or 
Pr-r =("'l'#'1"')

,,-, =(...,ro =0, Na =#,oa =0,...),
or briefly (2a)

Pr-1 = (. . ., 0, # ,0, ...)

where # is used as a symbol for both values 0 or l.
Rule D- can be reduced to the staremenr. if in the triad of

processes P holds - (M, N, O, ...) in the previous rime srrata

Pt:(M) = Pr_r(O)

then

Pf(N) =0,
in all other cases has the value of

Pr(N) = t

Further we will denote the presented
D;=*.

Situations conforming to (2) and (2a) will not reckon area
P, as free for implementation of investments in the time
period t (puts in decision maker position 0). Otherwise the
case investment admits (will insert l). In the other scheme the
conditions are displayed as a schematic table, see Thble I
(lines are time, columns are areas of realization). The positive
solution (investor is willing to invest) comes into being, when
it is valid that the market is implemented in the past period
investment partly in the neighbouring areas (segments) (in
Table I marked bold I substitutes'r,/r. # substitutes anl).

Table l: Decision rule, result action

If it is valid for the following time step that (market,
investment tnrst, competitors,..) who in the pretious period did
nlt mnhe an) inilestments in the neighbounng segtunts, then a

(3)

(4)

(5)

decision rule as

49



Acta Polytechnica Vol. 43 No. ll200g

dzcision-maker himself arbihaas likzwise negatinely (see Thble 2),
regardless whether he himself has already invested into the
given area.

Table 2: Decision rule, result no action

3 Implementation of a fully new
solution for a fully vacant market
Let us supposed that the solution for which a decision-

-maker decides is a new solution on a fully vacant demand
market. For example we have a fully new product like a mobile
phone on a totally fixedline phone market or a PC on the
mainfiame computer market a decade or two ago. Moreove4
the market area will not be limited in the near future by any
boundary conditions of demand, not even by market saturat-
ion. Diagrammatic calculations according to (3) to (5) are
shown in Thble 3 (a segment fiom a wider calculation of how
the market will fill up in time (vertical axis) and the space for
steering decisions for management shows the demand op-
portunities (horizontal axis)).

Applied decision criterion Dl=61 = (f, Oim(n)) is a criterion

of nvo dimensions, like those described in (3), (4), (5), where
F, tests whether in the last time period the investmenr. acrion
on the left of the decision-makers area occurred and F, tests
whether in the past time period the investment action to the
right of the decision-maker area occurred. The metrics of
evaluation dim(h) are given in this case as 1 and 0.

Table 3: Decision stmcture in time and decision area

The input data line ar rhe starting time, to in Thble 3 and
the first line in trig. I are followed by the generated decisions
(solutions) on the basis of decision criteria Dl=o refer to lines
t=1,2,... (in Thble 3) are graphically illusrrared in trig. I
(calculation created by MS Excel). For clariry, a parricular area
with valuation yes = | is introduced as a dark (red) circum-
scribed field, and areas with value no=O are presented as
clarity light (geen; fields withour a fiame. In the logic of
notation (l) we understand solutions as an area where fulfil-
ments were activated. Activity A has placed there qualities
and quantities (U,D,Q) , e.g., investment, capital assets, hous-
ing constructions, etc.

4Interpretation
The results in Table 3 and in Fig. I call for amention and

a deeper interpretation ofseveral interesting facts:

l. The action area on the basis of decision-making criteria
Dl=o is not homogeneous. If we are at the top of the

solution pyramid (in the time sequence from t= l, and
column.BI,/in Fig. l) the possibilities of factual expansion
of the Psp element in time (column in Table 3) are
considerably predetermined. The whole area of the future
development for the factualP(BW) series in Fig. I creates
an empty set in time. In other words, a new techni-
cal solution does not create a substantial development
chance; this is initially exploited mostly in related
branches of business. Dispersion in time has its own rules
and characteristics of diffusion.

2. In the area of solution dispersion in the series of capital
action blocks (on the basis of Df,=o application) relatively

extensive prohibitive areas without feasible implementat-
ion of any action are formed. In economic theory, the
termmarket bubbleshas been often used in the recent years.
In the presented situation we can talk about generating
such market bubbles in terms of inaccessibility or non-
-feasibility areas for decision implementation.

3. Each area of implementation for capital activity has a dit
ferent life-cycle in time. If individual series of actions
(A, B, C, ,.., M, N, O, ..., see vertical column in Figs. l, 2,
3, ...) are followed, we recognise that we have diflerent
ground schemes for the economic survival of actions.
Capital steps that we interpret here in a simplified man-
ner may be vital in its life-cycle only if and when, if their
cyclical renewal occurs. However, the renewal has differ-
ent (time) cycles and will offer good conditions for yields
in, e.g., relatively irregular time cycles, in the opposite
case, capital spending the on action must be terminated.

4. The lifetime of on activity in area X is not given only by
the life-cycle of the process P(o), or by means of standards
for on steering process L(r). It is conditioned by dzcision
criterian D- and its mechanism accepted for the steering
area and, aice uersa for management of the task. It is not
diffrcult to prove that almost every criterion forms another
fracnl sparc for exercising decision use in time and place.

5. A decision in some of the places @, B, C, ..., M, N, O, ...)
in time f will in future time periods influence a growing
spectmm of realization areas. See Fig. 3, in (f+ l) there
are only 3 areas ofrealization in prospect, in (f*2) there

Time / Space Decision making area (interventions)

Starting period
f=0 0 0 0 0 0 I 0 0 0 0 0 0

Consequence
decisions f = I 0 0 0 0 0 I 0 0 0 0 0

,_ c) 0 0 0 I 0 0 0 I 0 0 0 0

t=3 0 0 I 0 0 I 0 I 0 0 0

t=4 0 I 0 0 0 0 0 0 0 0 0

t=5 I 0 I 0 0 0 0 0 I 0 0

f=6 0 0 0 I 0 0 0 I 0 0 0 I

t=7 0 I 0 0 I 0 0 0

f=8 0 0 0 0 0 0 0 0 0 0 0 0

50



Acta Polytechnica Vol. 43 No. l/2002

Fig. l: Calculation of steering management decisions on the basis of decision rule D- in time and implementations areas

are 5 possible places of realisation, and in (f +3) there
exist 9 recommended places of realisation.

6. Decisions in each area (A., B, C, ..., M, N, O,...) are af-
fected by situations from past time periods by a growing
spectmm of implemented decisions, 3, 5, 9, .. . These are
schematic displayed in Fig. 2.

In area of non-permissible actions according to criterion
D- , actions of another identical type cannot start. Each inter-
section of decision rules is a new decision-making exercise
and a new application example of the fulfilment of steering
area and time.

5 Implementation of solutions on
a completely full market
The starting area of acrions P(o) at time t= 1 is referred as

a starting fields set, put into the model as @, B, C, ..., M, N, O,
. .., X, . ..). If such a set is totally engaged, i.e. for the action
area it is valid at fo as (1, l, 1, ..., l), it is not possibile to invest
in the future (on the basis of criteria Di=). Howeve4 maxi-
mum effort in fo withholds all potential capital assers in
the given areas in the future. The area of implementation
remains an empty set for all time.

Fig. 2: Decisions that will influence
time strata (concentration of
decisions)

an element in the eight's
influences from preceding

Fig. 3: Diffusion of decision-making consequences on strata and
areas of realisation followed in time

Fig. 4: Starting activity oftype (0, t, 0, l, 0, ...).The steering area
is an empty set for implementation of criteria {=o.

!6 tu i..|ffi dm.rqdd d.d.D
I c \t r0l , I

\
\

\
I

\,

5l



Acta Polytechnica Vol. 43 No. l/2003

6 A half-saturated market
The starting area of actions P(o) in time fo may be half-

-saturated, which means we will write
Pt(.) = 0,0, 1,0,...) .

Employment of capaciry hold back, as well as previous
situation, possibility to implement any further actions in
the given area (on the basis of the criterion Dl=). Fig. 4

represents data of a steering area that was half-saturated,
type (0, 1,0, l,0, ...).

The preliminary conclusion, is tfutt mnssiae star-ting,inaest-

mmt il,oes not paae way to successful and, sustninable inaestmtnt
oL e.g., productive market development, where the starting
steering area presents a partly saturated set (robust filling of
50 Vo of total capacity). In the course of decision rule we can
find the aarizt) of responses filling the steering area.

7 A sporadically occupied steering
area
Examples of other types of starting areas P(o) in time fo are

shown in Ftgs. 5, 6 and 7. Analyses of their technical-eco-
nomic interpretation would be quite interesting. A further
interesting question seems to arise. To what extent do steer-
ing models and decision-making as such influence the area
(management area) at which technical-economic activities
are accomplished? It would extend beyond the fiamework
of this paper to deal with this difiicult question. However,

the calculations figured in the figures display confirm that
it is a nan-honngeneous feasibility area for management deci-
sions, with the formatinn of bubbl.es in the space for decision
implementation.

Fig. 5: Steering area and decision rule {=o and starting area at f6

as (1,0,0,1,0,0,1, ...)

Fig. 6: Steering area and decision rule %.=o and starting area at fs

as (1,0,0,0, l,0,0,0, l, ...)

52

rrlt l

Fig. 7: Steering area and decision rule %.=c for starting area at fg

as (1,0,0,0, l,0,0,0, 1,...)

8 Conclusion
Existing economic and management theories of techni-

cal-economic processes are very often in practice based on the
perception of homogeneity in the application space. A num-
ber of technical and economic projects are outlined under
the condition that all areas of implementation of manage-
ment decisions are homogenous and rules that were valid
in analogous cases in the past will also be valid in possible fu-
ture designs and projects. The purpose of this paper was to
query such a conviction and indicate new possible directions
in the development of the discipline. This involves a new ori-
entation. The orientation will indicate new consideration
and direction of thinking. After model interpretation of de-
terministic chaos in management it. was necessary to see

the whole series and passages of the area of technical-eco-
nomic management in another light (Kubik 1982). Perhaps
the entire series of educationally splendid statements and
passages in textbooks have to be rearranged. In notat-
ion (l) the decision processes for selection of management
interventions may be seen as a meta-expression. Seeking
for principal differentiations and revisions may gain some

more argumentation. The presumption of homogeneiry
in the decision-making area may coincide with all decision
mechanisms. Competition evaluation, crisis management
decisions in a complicated technical-economic project lead
to new and newly-authorized doubts. It is, howevet possible
that it will be necessary ever in the near future to accept
approaches and changes in interpretations of more powerful
implementation of interferences between decision-making
and implementation areas. The applications of fiactals
(Mandelbrot l99l) may provide an interesting stimulus for
innovation.

Acknowledgement
This paper forms part of a research project of CTU in

Prague at the Faculty of Civil Engineering: Management
of the life cycle in sustainable development of construc-
tion, building enterprises and regions (MSM: 210000006), fi-
nanced by the Ministry for Education, Youth and Physical

Tiaining CR.

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:, t:ar,,:

:l:l::.,:
tr*il':



Acta Polytechnica Vol. 43 No. l/2002

In the calculation the paper makes use of the methodo_
Iogical basis created within the project on: ,,Harmonisation
engineering activities with EU (design merhod, and realizat_
ion of building Life-Cycle according to EU ISO).,financed by
the Grant Agency of the Czech Republic prague l, National
Avenue 3.

References

lll Mandelbrot, Benoit, 8.,8: Die fraktalz Geomztriz d,er
Nahtr. Basel, Boston, Berlin: Birkhiiuser Verlag, I g9 I .

t2l Kubik, S., Kotek, 2., Razim, M., Hruirik, J., BranZov-
skf , J.: Temiz automatickdho iizent II. ffheory of steering
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Beran, V.: Modcloudni a itrent 10. (Modzlkng and Manage-
mmt l0). Skripta iVUf. Praha: Vydavatelstvi iVUf
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Beran, Y.: Modcloudnta i{uri 20. (Modclling and, Maru,ge-
mmt 20). Skripta iVUf. Praha: Vydavatelstvi iVUf
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Mtek, J., Beran, V.: Automntizoaand systdmy itzcni. (Auto-
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Tondl, L.: Hodnoceni a hodnoty. (Metodologick6 roz-
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t3l

14)

t5l

t6l

t7l Beran, Y .: Proi a ekonomice nephtt I + I =2 . (Wh1 Does Not
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" 
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t8l Wolfram, S.: A New lfind, of Sciznu. Amazon, 2002.
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[l0] Beran V. a kol.: D1ru,mbbj hnrmanogratn, elektrsnithz roz-
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[1 l] Raitra, H., Schlaifer, R.: Arylied Statistital Decisbn Tlwory.
Cambridge, Mass.: Harvard University Press, 1961.

I 12] Bayes, T.: Essay towards Solaing a Problun in tfu Doctrinc of
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of 1763 paper).

Doc. Ing. Vilclav Beran, DrSc.
phone: +420224 354 526
e-mail: beran@fsv.cvut.cz

Department of Economics and Management
in Civil Engineering

Czech Technical University in Prague
Faculty of Civil Engineering
Thiikurova 7
166 29 Prague 6, Czech Republic

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