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

Technical-economic processes are generally events running in t'imc and spam (factual space of proposal opportunities). Most of these events are related to their economic W-qdt and their technical-economic structure. In their classical analytical form, models of technical-economic events are based on a description of systems of equations, matrix models, 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 theory (Demel, 2002), symbolical logic and sets (\y'lcek, Beran, 1984) and also on cellular automats (Wolfiam, 2002), or symbolic 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 oparational 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 terminology. Models used in practice restrict their own possible goals and describe almost exclusively already existing (known) reality. 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 construction costs [fCC), life-cycle cost (LCC), revenue per year (ROI), feasible construction time or cash flow schedule, diverse deadlines, or other quantifiable parameters have to appreciate existing design parameters, no matter how suitable 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


I Introduction
Technical-economic processes are generally events running 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 models, 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 theory (Demel, 2002), symbolical logic and sets (\y'lcek, Beran,  1984) and also on cellular automats (Wolfiam, 2002), or symbolic 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 oparational 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 terminology.Models used in practice restrict their own possible goals and describe almost exclusively already existing (known) reality.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 construction 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 suitable 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-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 available 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 present 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 behaviour of nonlinear economic processes (with cycles of balance and deterministic chaotic states, (Kubik, 1982)) offer interest- ing applications in economics.
Engineers and economistsusing simple modelling tools like timetables, budgeting, costing, and enterprise financial and management planningshould 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 instruments.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 represent and refer to those qualities that have can create innovative design, create an added value over a technical-economic common (standard) design and solutions.Economics, unlike technical science, presents values that are partly impermanent (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) simultaneously 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 processcomponents 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 diffrcult 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, 1997and 1999).To simplify the situation, the operating model will be described only in the space of quantitative derived components created on the basis of so called networked processes P,+ N, <A, K>, where A represents a set of components 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) 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 ( Pprocess), 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 Kis able to implement in time all necessary communication about managerial information and interventions.Khas 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 concerning 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 engineering 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 decision-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 Drules.This is the main reason why this article was written.

AN
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)  Before we try to reply to this cluster of questions, it is desirable 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 necessarily 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 scenarios, etc.).
For the purposes of furrher explanation it will not be necessary to distinguish among particular phases or sophistication 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- .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 { ",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 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 ofequations (linea6 nonJinea4 ordinary differential equarions, etc.) that. are well-known in classical mathematics.In economic practice there are many situations where a description of relevant 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 decisions (implementation of decision interventions) may be done more carefully, ifwe are aware of the complicated application 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).
Rule Dcan 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).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 dzcision-maker himself arbihaas likzwise negatinely (see Thble 2), regardless whether he himself has already invested into the given area.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. 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  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 implementation 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.Capital steps that we interpret here in a simplified manner may be vital in its life-cycle only if and when, if their cyclical renewal occurs.However, the renewal has different (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.

3.
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 Dand 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.

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

Conclusion
Existing economic and management theories of technical-economic processes are very often in practice based on the perception of homogeneity in the application space.A number 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 future 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 orientation.The orientation will indicate new consideration and direction of thinking.After model interpretation of deterministic chaos in management it.was necessary to see the whole series and passages of the area of technical-economic management in another light (Kubik 1982).Perhaps the entire series of educationally splendid statements and passages in textbooks have to be rearranged.In notation (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), financed by the Ministry for Education, Youth and Physical Tiaining CR. :l:l::.,:tr*il': 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, marking decant SG€tr1, Sc€t12, ... (see (lc)), > xi,fx j,..) optim_ SGen3(ld) 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.

Fig. l :
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.

Fig. 2 :Fig. 7 :
Fig. 2: Decisions that will influence time strata (concentration of decisions) influence the deci- sion steering process D-? 3. To what extent is decision process Din time srrata t, affected by the previous decisions in the former time

Table l :
Decision rule, result action

Table 2 :
Decision rule, result no action 3 Implementation of a fully new solution for a fully vacant market

Table 3 :
Decision stmcture in time and decision area 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.