This is a possible 3rd step in the overall topic ‘Co-Learning python3′. After downloading WinPython and activating the integrated editor ‘spyder’ (see here), one can edit another simple program dealing with population dynamics in a most simple way (see the source code below under the title ‘EXAMPLE: pop0d.py’). This program is a continuation of the program pop0.py, which has been described here.
COMMENTS
In this post I comment only on the changes between the actual program and the version before.
IMPORTS
In the new version two libraries are used:
import matplotlib.pyplot as plt # Lib for plotting import numpy as np # Lib for math
This extends the set of possible functions by functions for plotting and some more math.
MORE INPUT DATA
In this version one can enter a base year, thus allowing a direct relation to real year numbers in the history or the future. In the example run at the end of the program I am using the official population numbers of the UN for the world population in the years 2016 and 2017, which will hypothetically be forecasted for 15 years.
MORE GLOBAL VARIABLES
As explained in the previous version there are local variables restricted in their meaning to a certain function and global variables outside a function. In this version a datastructure called pop is introduced to store information in a sequential order. In this case the first population number given in the variable p is stored on the first position by the append operation which is part of the data structure pop.
pop = [] # storage for the pop-numbers for plotting pop.append(p)
Later a data structure x is needed a s a sequence of consecutive numbers starting with 1, ending with the number of entries in the pop data strucure and with as many positions as pop has entries. The number of elements in pop can be computed by applying the len() operator to pop.
x = np.linspace(1,len(pop),len(pop))
EXTENDED COMPUTATION
The computation has extended a little bit by the new line appending the actual value of p to the pop storage:
for i in range(n): p=pop0(p,br,dr) pop.append(p)
Thus the pop data structure stores every new population value in p in the sequential order of its occurence. Moreover it has been here realized a loop with the for function. The variable i is running through a sequence of values provided by the range() function. This function builts an array of numbers from 0 to n-1 and repeats therefor the call of pop0() n times.
But the most important point here is that the value of the global variable p is handed over to the function pop0(), and the local value of the variable p inside the pop0() function is again handed over by the return command to the outside of the function. Outside there is waiting the global p variable which is receiving the new value by the =-operation. In the next call of pop0() pop0() receives as new input the global variable p with the new value.
SHOWING THE RESULTS
There are now three different data show actions: (i) the list of all years with their numbers, (ii) a graphical plot of data points, and (iii) a print out of the increase of the population compared the final result with the base year.
LIST OF ALL YEARS
for i in range(n+1): print(‘Year %5d = Citizens. %8d \n’ %(baseYear+i, pop[i]) )
Again is the for function in action ranging through the number of cycles given by the variable n. The number of the population in the different years is catched from the pop data structure by indexing the individual elements of pop by the bracket command []. pop[i] represents the i-th element of pop.
PLOTING THE POPULATION VALUES
x = np.linspace(1,len(pop),len(pop)) plt.plot(x, pop, ‘bo’) plt.show()
The plotfunction plot of the library plt needs an arry of numbers for the x-axis given here by the x data structure, an arry of numbers for the y-axis given here by the pop-data structures, and optionally some parameter for the format of the plot symbolds. Here with ‘bo’ a black small circle. After all values are prepared will the command plt.show() make the plotted data visible.
Plot with the UN data for the possible growth of the world population
print(“From Year %5d, until Year %5d, a change of %2.2f percent \n” % (baseYear, baseYear+n,Increase) )
Getting the base year from pop[0] and the last year from pop[len(pop)-1] one can compute the difference as the increase translated into a percentage.
REAL DATA
Although the population program is still very simple it is usefull to compute real numbers of the real world. One example are the official data of the United Nations (UN) which are collecting world wide data since their foundation 1948.
But, as a first surprise, although the UN provides lots of data from all the countries world wide, they do not systematicall a birthrate (br) or a deathrate (dr). Thus I have compiled the br and dr by inferring it from absolute population numbers from 2016 and 2017 combined with the fertility rate for 1000 people in the year 2013 averaged over all countries. This gives an estimate of 3% for the birthrate br. Using this from 2016 to 2017 this gives an ‘overshoot’ to the real numbers of 2017. I inferred from this the deathrate dr which is clearly a very week inference. Nevertheless it works for 2016 to 2017 and gives a first simple example for the upcoming years.
This is a possible 2nd step in the overall topic ‘Co-Learning python3′. After downloading WinPython and activating the integrated editor ‘spyder’ (see here), one can edit a first simple program dealing with population dynamics in a most simple way (see the source code below under the title ‘EXAMPLE: pop0.py’.
BASIC PROGRAMMING ELEMENTS
SOURCE FILE
The source code is stored under the name ‘pop0.py’. It is a ‘stand alone’ program not making use of any kind of a library except the built in functions of python3. The external libraries can be included by the ‘import command’.
FUNCTION DEFINITION
If one wants to use the built-in functions in some new way one can do this by telling the computer the keyword ‘def‘ which states that the following text defines a new function.
A function has always a name, some input arguments between rounded brackets followed by a colon marking the beginning of the function-body. After the colon follows a list of built-in commands or some already defined functions. With defined functions one can make life much easier. Instead of repeating all the commands of the function-body again and again one can limit the writing to the call of the function name with its input arguments.
In the example file we have the following function definition:
def pop0(p,br,dr): p=p+(p*br)-(p*dr) return p
The name is pop0, the input arguments are (p,br,dr), the used built-in functions are +, -, *, return as well as the =-sign, and the new composition is p=p+(p*br)-(p*dr). This new composition combines known function names with new variable names to compute a certain mathematical mapping. The result of this simple mapping is stored in the variable ‘p’ and it is delivered to the outside of the function pop0 by the return-statement.
NECESSARY INPUT VALUES
To run the program one has to call the defined new function. But because the called function will need some values for the input variables one has first to enable the user to interact with the program by some input commands.
The input commands are informing the user which kind of information is asked for and the answers of the users will be stored in the variables p, br, and dr. The input values can also be ‘casted‘ into different value types like int — for integer — and float — for floating point –.
Here is the protocol of a possible input:
Number of citizens in the start year? 1000
Birthrate %? 0.82
Deathrate in %? 0.92
CALLING A DEFINED FUNCTION
After these preparations one can call the defined new function with the statement pnew = pop0(p,br,dr). Because the input variables have values received from the user the new function can start it’s mapping and can compute the follow up value for the population.
SHOW RESULTS
To show the user this new value explicitly on the screen one has to use the print function, the counterpart to the input function: print(‘New population number:\n’,int(pnew)). The print function prints the new value for the population number on the screen. If you look closer to the print function you can detect some inherent structure: print() is the main structure with the function name ‘print’ and the brackets () as the placeholder for possible input arguments. In the used example the input arguments have two ‘parts’: (‘…’,v). The ‘…’-part allows some text which will be shown to the user, in our case New population number: followed by a line break caused by the symbols \n. The v-part allows the names of variables which have some values which can be printed. In the used example we have the expression int(pnew). pnew is the name of a variable with a value delivered by the pop0() function enabled by the return p function of the pop0() function. (Attention: the ‘return’ function works without ()-brackets to receive input arguments! The variable ‘p’ is an input argument) The value delivered by ‘return p’ is a floating point value. But because we have only ‘whole citizens’ we cast the non-integer parts of the float value away by making the variable ‘pnew’ an argument of another function int(). The int() function translates a floating point value into an integer value. This is the reason that we do not see ‘999.0’ but ‘999’:
New population number: 999
REMARK: Global and Local Variables
This simple example tells already something about the difference between global and local values. If one enters the variable names ‘p’ and ‘pnew’ in the python console of the spyder editor then one can see the following:
p Out[5]: 1000
pnew Out[6]: 999.0
After the function call to pop0() the original value of ‘p’ is unchanged, and the new value ‘pnew’ is different. That means the new value of ‘p internal in the function’ and the ‘old value of p external to the function’ are separated. The name of a variable has therefore to be distinguished with regard to the actual context: The same name in different contexts (inside a function definition or outside) does represents different memory spaces. The variable names inside a function definition are called local variables and the variable names external to a function definition are called global variables.
The context of this small initiative ist the Distributed-Actor-Actor Interaction (DAAI) paradigm described in this blog. For this AAI paradigm one will need an assisting software to manage real problems with real people. In principle this software can be realized by every kind of programming language. Which one will be used for the planned overall software service will be decided by those groups, which will do the final job. But for the development of the ideas, for an open learning process, we will use here the programming language python 3. To get a first understanding of the main programming languages and the special advantages and disadvantages of python 3 compared to python 2 and to other important languages see the short overview here 2019-03-18 in apenwarr or chapters 1-2 from the excellent book by Lutz mentioned below.
VISION
There are many gifted young people around who could produce wonderfull programms, if they would find a ‘start’, how to begin, where to go from here… sometimes these are friends doing other things together — like playing computer games 🙂 — why not start doing programming real programs by themselves? Why not start together some exciting project by their own?
If they want to do it, they need some first steps to enter the scene. This is the idea behind this vision: getting young people to do first steps, helping each other, document the process, improve, making nice things…
CONTRIBUTING POSTS
Here is a list of posts which can support the Co-Learning Process.
This code is completely experimental! The only intention is to show how the ideas of the theory are working, because for most people the theory is too complex to be easily understood. That’s normal life: as complexity and innovation is raising the understanding is going down …
From minimal to basic, first step: multiple state descriptions and multiple rule documents can be unified for simulation. (last change: Oct 21, 2020)
New Class Simulation. The first simple simulation mode is now possible. (Last change: September 16, 2020)
New Class ‘Storage’ to allow storing and loading of documents. This will be needed for the simulator. (last change: Sept 14, 2020)
New Class ‘Xrules’ for the change rules. Allows the editing of first very simple rules (the development of the x-rules will be in the future an ‘infinite’ story …)(last change: September 13, 2020)
Classes only ...: Overview of the program and collecting console-output in classes too (the real interaction will be with an interactive website). (last change: Sept 11, 2020)
STARTING WITH PYTHON3 – The very beginning – part10: Using the git system: For the further development of our first demo we have started a simple server with a git system. All the upcoming work will use this. Attention: the server ha changed! Look for a new presence in a few weeks under the address epolitics4you.org (Last change June-21, 2020)
STARTING WITH PYTHON3 – The very beginning – part 9: In part 9 the concept of a simple virtual world has been re-analyzed with the conceptual framework of the AI paradigm thereby enabling a first encounter of the AAI paradigm with the python programming language. It is a complete, working program, nevertheless still simple. In the next posts this will be further extended.
STARTING WITH PYTHON3 – The very beginning – part 8: In part 8 the world cycle is started. In this post it is described how one can manage the energy level of all important objects. In case of the actors these will be removed from the grid and from the actor list when they reach energy level <1. Thus, there is real death in this simple world. To survive you must be able to survive. This points to the mystery of life in this universe…
STARTING WITH PYTHON3 – The very beginning – part 7: Continuation of editing the 2D virtual world as a grid. A major improvement is the automatic collection of all objects according to their types from the grid as well as the automatic association with individual object values including an individual ID. Thus with only one line one can manage the layout of a whole world filled with individual objects and actors… you will see.
STARTING WITH PYTHON3 – The very beginning – part 6: Editing a 2-dimensional virtual world in a grid format for further experiments. Yes, from this post onward I start to develop a complete virtual world (2D) with actors having an increasing amount of artificial intelligence (AI) built in, not only machine learning (ML), but also more and more a cognitive architecture (CA) enabling real semiotic machines (SM).
STARTING WITH PYTHON3 – The very beginning – part 5: More strings, sequences, control-statements exemplified with a simple input-output actor which shows some creativity… and some glimpse of cognitive entropy.
STARTING WITH PYTHON3 – The very beginning – part 4: Introducing strings and some operations with them. The examples are embedded in a small python program simulating a simple input-output actor which is completely deterministic.
STATING WITH PYTHON3 – The very beginning – part 3: Introducing first data types and operations with them. From this post onwards I am using only the windows powershell, an ordinary editor (notepad or notepad++), and python activated from the windows powershell. This is all you need :-). By the way: transferring the used files to Linux — I am using the distribution ubuntu 18.04 LTS — you can work directly without any changes. Instead of the windows powershell you have in Linux your standard systems terminal, calling python you can distinguish between ‘python’ (2.7) ‘python 3’ (3.6), and there are many good editors (I am using jPad, but there are many more). That’s it. Thus you can switch between Linux and windows 10 without any problems.
Example python3: popShow0 – simple file-reader: This small program allows the opening of a file browser to select a file according to its filename. Then the program reads this file and makes a formatted output.
More posts will follow in a random order depending of the questions which will arise or the ideas someone wants to test.
REFERENCES
There is a huge amount of python books, articles and online resources. I will mention here only some of the books and resources I have used. The following is therefore neither complete nor closed. I will add occasionally some titles.
Mark Lutz, Learn Python, 2013,5th ed.,Sebastopol (CA), O’Reilly
Mark Lutz, Programming Python, 2011, 4th ed., Sebastopol (CA), O’Reilly
Last change: 28.February 2019 (Several corrections)
CONTEXT
An overview to the enhanced AAI theory version 2 you can find here. In this post we talk about the special topic how to proceed in a bottom-up approach.
BOTTOM-UP: THE GENERAL BLUEPRINT
Figure 1: Outline of the process how to generate an AS with a bottom-up approach
As the introductory figure shows it is assumed here that there is a collection of citizens and experts which offer their individual knowledge, experiences, and skills to ‘put them on the table’ challenged by a given problem P.
This knowledge is in the beginning not structured. The first step in the direction of an actor story (AS) is to analyze the different contributions in a way which shows distinguishable elements with properties and relations. Such a set of first ‘objects’ and ‘relations’ characterizes a set of facts which define a ‘situation’ or a ‘state’ as a collection of ‘facts’. Such a situation/ state can also be understood as a first simple ‘model‘ as response to a given problem. A model is as such ‘static‘; it describes what ‘is’ at a certain point of ‘time’.
In a next step the group has to identify possible ‘changes‘ which can be associated with at least one fact. There can be many possible changes which eventually need different durations to come into effect. These effects can happen as ‘exclusive alternatives’ or in ‘parallel’. Apply the possible changes to a situation generates ‘successors’ to the actual situation. A sequence of situations generated by applied changes is usually called a ‘simulation‘.
If one allows the interaction between real actors with a simulation by associating a real actor to one of the actors ‘inside the simulation’ one is turning the simulation into an ‘interactive simulation‘ which represents basically a ‘computer game‘ (short: ‘egame‘).
One can use interactive simulations e.g. to (i) learn about the dynamics of a model, to (ii) test the assumptions of a model, to (iii) test the knowledge and skills of the real actors.
Making new experiences with a simulation allows a continuous improvement of the model and its change rules.
Additionally one can include more citizens and experts into this process and one can use available knowledge from databases and libraries.
EPISTEMOLOGY OF CONCEPTS
Fig.2: Epistemology of concepts used in an AAI Analysis process
As outlined in the preceding section about the blueprint of a bottom-up process there will be a heavy usage of concepts to describe state of affairs.
The literature about this topic in philosophy as well as many scientific disciplines is overwhelmingly and therefore this small text here can only be a ‘pointer’ into a complex topic. Nevertheless I will use exactly this pointer to explore this topic further.
While the literature is mainly dealing with more or less specific partial models, I am trying here to point out a very general framework which fits to a more genera philosophical — especially epistemological — view as well as gives respect to many results of scientific disciplines.
The main dimensions here are (i) the outside external empirical world, which connects via sensors to the (ii) internal body, especially the brain, which works largely ‘unconscious‘, and then (iii) the ‘conscious‘ part of he brain.
The most important relationship between the ‘conscious’ and the ‘unconscious’ part of the brain is the ability of the unconscious brain to transform automatically incoming concrete sens-experiences into more ‘abstract’ structures, which have at least three sub-dimensions: (i) different concrete material, (ii) a sub-set of extracted common properties, (iii) different sets of occurring contexts associated with the different subsets. This enables the brain to extract only a ‘few’ abstract structures (= abstract concepts) to deal with ‘many’ concrete events. Thus the abstract concept ‘chair’ can cover many different concrete chairs which have only a few properties in common. Additionally the chairs can occur in different ‘contexts’ associating them with different ‘relations’ which can specify possible different ‘usages’ of the concept ‘chair’.
Thus, if the actor perceives something which ‘matches’ some ‘known’ concept then the actor is not only conscious about the empirical concrete phenomenon but also simultaneously about the abstract concept which will automatically be activated. ‘Immediately’ the actor ‘knows’ that this empirical something is e.g. a ‘chair’. Concrete: this concrete something is matching an abstract concept ‘chair’ which can as such cover many other concrete things too which can be as concrete somethings partially different from another concrete something.
From this follows an interesting side effect: while an actor can easily decide, whether a concrete something is there (“it is the case, that” = “it is true”) or not (“it is not the case, that” = “it isnot true” = “it is false”), an actor can not directly decide whether an abstract concept like ‘chair’ as such is ‘true’ in the sense, that the concept ‘as a whole’ corresponds to concrete empirical occurrences. This depends from the fact that an abstract concept like ‘chair’ can match with a nearly infinite set of possible concrete somethings which are called ‘possible instances’ of the abstract concept. But a human actor can directly ‘check’ only a ‘few’ concrete somethings. Therefore the usage of abstract concepts like ‘chair’, ‘house’, ‘bottle’ etc. implies inherently an ‘open set’ of ‘possible’ concrete exemplars and therefor is the usage of such concepts necessarily a ‘hypothetical’ usage. Because we can ‘in principle’ check the real extensions of these abstract concepts in everyday life as long there is the ‘freedom’ to do such checks, we are losing the ‘truth’ of our concepts and thereby the basis for a realistic cooperation, if this ‘freedom of checking’ is not possible.
If some incoming perception is ‘not yet known’, because nothing given in the unconsciousness does ‘match’, it is in a basic sens ‘new’ and the brain will automatically generate a ‘new concept’.
THE DIMENSION OF MEANING
In Figure 2 one can find two other components: the ‘meaning relation’ which maps concepts into ‘language expression’.
Language expressions inside the brain correspond to a diversity of visual, auditory, tactile or other empirical event sequences, which are in use for communicative acts.
These language expressions are usually not ‘isolated structures’ but are embedded in relations which map the expression structures to conceptual structures including the different substantiations of the abstract concepts and the associated contexts. By these relations the expressions are attached to the conceptual structures which are called the ‘meaning‘ of the expressions and vice versa the expressions are called the ‘language articulation’ of the meaning structures.
As far as conceptual structures are related via meaning relations to language expressions then a perception can automatically cause the ‘activation’ of the associated language expressions, which in turn can be uttered in some way. But conceptual structures can exist (especially with children) without an available meaning relation.
When language expressions are used within a communicative act then their usage can activate in all participants of the communication the ‘learned’ concepts as their intended meanings. Heaving the meaning activated in someones ‘consciousness’ this is a real phenomenon for that actor. But from the occurrence of concepts alone does not automatically follow, that a concept is ‘backed up’ by some ‘real matter’ in the external world. Someone can utter that it is raining, in the hearer of this utterance the intended concepts can become activated, but in the outside external world no rain is happening. In this case one has to state that the utterance of the language expressions “Look, its raining” has no counterpart in the real world, therefore we call the utterance in this case ‘false‘ or ‘not true‘.
THE DIMENSION OF TIME
Fig.3: The dimension of time based on past experience and combinatoric thinking
The preceding figure 2 of the conceptual space is not yet complete. There is another important dimension based on the ability of the unconscious brain to ‘store’ certain structures in a ‘timely order’ which enables an actor — under certain conditions ! — to decide whether a certain structure X occurred in the consciousness ‘before’ or ‘after’ or ‘at the same time’ as another structure Y.
Evidently the unconscious brain is able do exactly this: (i) it can arrange the different structures under certain conditions in a ‘timely order’; (ii) it can detect ‘differences‘ between timely succeeding structures; the brain (iii) can conceptualize these changes as ‘change concepts‘ (‘rules of change’), and it can can classify different kinds of change like ‘deterministic’, ‘non-deterministic’ with different kinds of probabilities, as well as ‘arbitrary’ as in the case of ‘free learning systems‘. Free learning systems are able to behave in a ‘deterministic-like manner’, but they can also change their patterns on account of internal learning and decision processes in nearly any direction.
Based on memories of conceptual structures and derived change concepts (rules of change) the unconscious brain is able to generate different kinds of ‘possible configurations’, whose quality is depending from the degree of dependencies within the ‘generating criteria’: (i) no special restrictions; (ii) empirical restrictions; (iii) empirical restrictions for ‘upcoming states’ (if all drinkable water would be consumed, then one cannot plan any further with drinkable water).
Last change: 23.February 2019 (continued the text)
Last change: 24.February 2019 (extended the text)
CONTEXT
In the overview of the AAI paradigm version 2 you can find this section dealing with the philosophical perspective of the AAI paradigm. Enjoy reading (or not, then send a comment :-)).
THE DAILY LIFE PERSPECTIVE
The perspective of Philosophy is rooted in the everyday life perspective. With our body we occur in a space with other bodies and objects; different features, properties are associated with the objects, different kinds of relations an changes from one state to another.
From the empirical sciences we have learned to see more details of the everyday life with regard to detailed structures of matter and biological life, with regard to the long history of the actual world, with regard to many interesting dynamics within the objects, within biological systems, as part of earth, the solar system and much more.
A certain aspect of the empirical view of the world is the fact, that some biological systems called ‘homo sapiens’, which emerged only some 300.000 years ago in Africa, show a special property usually called ‘consciousness’ combined with the ability to ‘communicate by symbolic languages’.
Figure 1: General setting of the homo sapiens species (simplified)
As we know today the consciousness is associated with the brain, which in turn is embedded in the body, which is further embedded in an environment.
Thus those ‘things’ about which we are ‘conscious’ are not ‘directly’ the objects and events of the surrounding real world but the ‘constructions of the brain’ based on actual external and internal sensor inputs as well as already collected ‘knowledge’. To qualify the ‘conscious things’ as ‘different’ from the assumed ‘real things’ ‘outside there’ it is common to speak of these brain-generated virtual things either as ‘qualia’ or — more often — as ‘phenomena’ which are different to the assumed possible real things somewhere ‘out there’.
PHILOSOPHY AS FIRST PERSON VIEW
‘Philosophy’ has many facets. One enters the scene if we are taking the insight into the general virtual character of our primary knowledge to be the primary and irreducible perspective of knowledge. Every other more special kind of knowledge is necessarily a subspace of this primary phenomenological knowledge.
There is already from the beginning a fundamental distinction possible in the realm of conscious phenomena (PH): there are phenomena which can be ‘generated’ by the consciousness ‘itself’ — mostly called ‘by will’ — and those which are occurring and disappearing without a direct influence of the consciousness, which are in a certain basic sense ‘given’ and ‘independent’, which are appearing and disappearing according to ‘their own’. It is common to call these independent phenomena ’empirical phenomena’ which represent a true subset of all phenomena: PH_emp ⊂ PH. Attention: These empirical phenomena’ are still ‘phenomena’, virtual entities generated by the brain inside the brain, not directly controllable ‘by will’.
There is a further basic distinction which differentiates the empirical phenomena into those PH_emp_bdy which are controlled by some processes in the body (being tired, being hungry, having pain, …) and those PH_emp_ext which are controlled by objects and events in the environment beyond the body (light, sounds, temperature, surfaces of objects, …). Both subsets of empirical phenomena are different: PH_emp_bdy ∩ PH_emp_ext = 0. Because phenomena usually are occurring associated with typical other phenomena there are ‘clusters’/ ‘pattern’ of phenomena which ‘represent’ possible events or states.
Modern empirical science has ‘refined’ the concept of an empirical phenomenon by introducing ‘standard objects’ which can be used to ‘compare’ some empirical phenomenon with such an empirical standard object. Thus even when the perception of two different observers possibly differs somehow with regard to a certain empirical phenomenon, the additional comparison with an ’empirical standard object’ which is the ‘same’ for both observers, enhances the quality, improves the precision of the perception of the empirical phenomena.
From these considerations we can derive the following informal definitions:
Something is ‘empirical‘ if it is the ‘real counterpart’ of a phenomenon which can be observed by other persons in my environment too.
Something is ‘standardized empirical‘ if it is empirical and can additionally be associated with a before introduced empirical standard object.
Something is ‘weak empirical‘ if it is the ‘real counterpart’ of a phenomenon which can potentially be observed by other persons in my body as causally correlated with the phenomenon.
Something is ‘cognitive‘ if it is the counterpart of a phenomenon which is not empirical in one of the meanings (1) – (3).
It is a common task within philosophy to analyze the space of the phenomena with regard to its structure as well as to its dynamics. Until today there exists not yet a complete accepted theory for this subject. This indicates that this seems to be some ‘hard’ task to do.
BRIDGING THE GAP BETWEEN BRAINS
As one can see in figure 1 a brain in a body is completely disconnected from the brain in another body. There is a real, deep ‘gap’ which has to be overcome if the two brains want to ‘coordinate’ their ‘planned actions’.
Luckily the emergence of homo sapiens with the new extended property of ‘consciousness’ was accompanied by another exciting property, the ability to ‘talk’. This ability enabled the creation of symbolic languages which can help two disconnected brains to have some exchange.
But ‘language’ does not consist of sounds or a ‘sequence of sounds’ only; the special power of a language is the further property that sequences of sounds can be associated with ‘something else’ which serves as the ‘meaning’ of these sounds. Thus we can use sounds to ‘talk about’ other things like objects, events, properties etc.
The single brain ‘knows’ about the relationship between some sounds and ‘something else’ because the brain is able to ‘generate relations’ between brain-structures for sounds and brain-structures for something else. These relations are some real connections in the brain. Therefore sounds can be related to ‘something else’ or certain objects, and events, objects etc. can become related to certain sounds. But these ‘meaning relations’ can only ‘bridge the gap’ to another brain if both brains are using the same ‘mapping’, the same ‘encoding’. This is only possible if the two brains with their bodies share a real world situation RW_S where the perceptions of the both brains are associated with the same parts of the real world between both bodies. If this is the case the perceptions P(RW_S) can become somehow ‘synchronized’ by the shared part of the real world which in turn is transformed in the brain structures P(RW_S) —> B_S which represent in the brain the stimulating aspects of the real world. These brain structures B_S can then be associated with some sound structures B_A written as a relation MEANING(B_S, B_A). Such a relation realizes an encoding which can be used for communication. Communication is using sound sequences exchanged between brains via the body and the air of an environment as ‘expressions’ which can be recognized as part of a learned encoding which enables the receiving brain to identify a possible meaning candidate.
DIFFERENT MODES TO EXPRESS MEANING
Following the evolution of communication one can distinguish four important modes of expressing meaning, which will be used in this AAI paradigm.
VISUAL ENCODING
A direct way to express the internal meaning structures of a brain is to use a ‘visual code’ which represents by some kinds of drawing the visual shapes of objects in the space, some attributes of shapes, which are common for all people who can ‘see’. Thus a picture and then a sequence of pictures like a comic or a story board can communicate simple ideas of situations, participating objects, persons and animals, showing changes in the arrangement of the shapes in the space.
Figure 2: Pictorial expressions representing aspects of the visual and the auditory sens modes
Even with a simple visual code one can generate many sequences of situations which all together can ‘tell a story’. The basic elements are a presupposed ‘space’ with possible ‘objects’ in this space with different positions, sizes, relations and properties. One can even enhance these visual shapes with written expressions of a spoken language. The sequence of the pictures represents additionally some ‘timely order’. ‘Changes’ can be encoded by ‘differences’ between consecutive pictures.
FROM SPOKEN TO WRITTEN LANGUAGE EXPRESSIONS
Later in the evolution of language, much later, the homo sapiens has learned to translate the spoken language L_s in a written format L_w using signs for parts of words or even whole words. The possible meaning of these written expressions were no longer directly ‘visible’. The meaning was now only available for those people who had learned how these written expressions are associated with intended meanings encoded in the head of all language participants. Thus only hearing or reading a language expression would tell the reader either ‘nothing’ or some ‘possible meanings’ or a ‘definite meaning’.
Figure 3: A written textual version in parallel to a pictorial version
If one has only the written expressions then one has to ‘know’ with which ‘meaning in the brain’ the expressions have to be associated. And what is very special with the written expressions compared to the pictorial expressions is the fact that the elements of the pictorial expressions are always very ‘concrete’ visual objects while the written expressions are ‘general’ expressions allowing many different concrete interpretations. Thus the expression ‘person’ can be used to be associated with many thousands different concrete objects; the same holds for the expression ‘road’, ‘moving’, ‘before’ and so on. Thus the written expressions are like ‘manufacturing instructions’ to search for possible meanings and configure these meanings to a ‘reasonable’ complex matter. And because written expressions are in general rather ‘abstract’/ ‘general’ which allow numerous possible concrete realizations they are very ‘economic’ because they use minimal expressions to built many complex meanings. Nevertheless the daily experience with spoken and written expressions shows that they are continuously candidates for false interpretations.
FORMAL MATHEMATICAL WRITTEN EXPRESSIONS
Besides the written expressions of everyday languages one can observe later in the history of written languages the steady development of a specialized version called ‘formal languages’ L_f with many different domains of application. Here I am focusing on the formal written languages which are used in mathematics as well as some pictorial elements to ‘visualize’ the intended ‘meaning’ of these formal mathematical expressions.
Fig. 4: Properties of an acyclic directed graph with nodes (vertices) and edges (directed edges = arrows)
One prominent concept in mathematics is the concept of a ‘graph’. In the basic version there are only some ‘nodes’ (also called vertices) and some ‘edges’ connecting the nodes. Formally one can represent these edges as ‘pairs of nodes’. If N represents the set of nodes then N x N represents the set of all pairs of these nodes.
In a more specialized version the edges are ‘directed’ (like a ‘one way road’) and also can be ‘looped back’ to a node occurring ‘earlier’ in the graph. If such back-looping arrows occur a graph is called a ‘cyclic graph’.
Fig.5: Directed cyclic graph extended to represent ‘states of affairs’
If one wants to use such a graph to describe some ‘states of affairs’ with their possible ‘changes’ one can ‘interpret’ a ‘node’ as a state of affairs and an arrow as a change which turns one state of affairs S in a new one S’ which is minimally different to the old one.
As a state of affairs I understand here a ‘situation’ embedded in some ‘context’ presupposing some common ‘space’. The possible ‘changes’ represented by arrows presuppose some dimension of ‘time’. Thus if a node n’ is following a node n indicated by an arrow then the state of affairs represented by the node n’ is to interpret as following the state of affairs represented in the node n with regard to the presupposed time T ‘later’, or n < n’ with ‘<‘ as a symbol for a timely ordering relation.
Fig.6: Example of a state of affairs with a 2-dimensional space configured as a grid with a black and a white token
The space can be any kind of a space. If one assumes as an example a 2-dimensional space configured as a grid –as shown in figure 6 — with two tokens at certain positions one can introduce a language to describe the ‘facts’ which constitute the state of affairs. In this example one needs ‘names for objects’, ‘properties of objects’ as well as ‘relations between objects’. A possible finite set of facts for situation 1 could be the following:
TOKEN(T1), BLACK(T1), POSITION(T1,1,1)
TOKEN(T2), WHITE(T2), POSITION(T2,2,1)
NEIGHBOR(T1,T2)
CELL(C1), POSITION(1,2), FREE(C1)
‘T1’, ‘T2’, as well as ‘C1’ are names of objects, ‘TOKEN’, ‘BACK’ etc. are names of properties, and ‘NEIGHBOR’ is a relation between objects. This results in the equation:
These facts describe the situation S1. If it is important to describe possible objects ‘external to the situation’ as important factors which can cause some changes then one can describe these objects as a set of facts in a separated ‘context’. In this example this could be two players which can move the black and white tokens and thereby causing a change of the situation. What is the situation and what belongs to a context is somewhat arbitrary. If one describes the agriculture of some region one usually would not count the planets and the atmosphere as part of this region but one knows that e.g. the sun can severely influence the situation in combination with the atmosphere.
Fig.7: Change of a state of affairs given as a state which will be enhanced by a new object
Let us stay with a state of affairs with only a situation without a context. The state of affairs is a ‘state’. In the example shown in figure 6 I assume a ‘change’ caused by the insertion of a new black token at position (2,2). Written in the language of facts L_fact we get:
Thus the new state S2 is generated out of the old state S1 by unifying S1 with the set of new facts: S2 = S1 ∪ {TOKEN(T3), BLACK(T3), POSITION(2,2), NEIGHBOR(T3,T2)}. All the other facts of S1 are still ‘valid’. In a more general manner one can introduce a change-expression with the following format:
This can be read as follows: The follow-up state S2 is generated out of the state S1 by adding to the state S1 the set of facts { … }.
This layout of a change expression can also be used if some facts have to be modified or removed from a state. If for instance by some reason the white token should be removed from the situation one could write:
These simple examples demonstrate another fact: while facts about objects and their properties are independent from each other do relational facts depend from the state of their object facts. The relation of neighborhood e.g. depends from the participating neighbors. If — as in the example above — the object token T2 disappears then the relation ‘NEIGHBOR(T1,T2)’ no longer holds. This points to a hierarchy of dependencies with the ‘basic facts’ at the ‘root’ of a situation and all the other facts ‘above’ basic facts or ‘higher’ depending from the basic facts. Thus ‘higher order’ facts should be added only for the actual state and have to be ‘re-computed’ for every follow-up state anew.
If one would specify a context for state S1 saying that there are two players and one allows for each player actions like ‘move’, ‘insert’ or ‘delete’ then one could make the change from state S1 to state S2 more precise. Assuming the following facts for the context:
PLAYER(PB1), PLAYER(PW1), HAS-THE-TURN(PB1)
In that case one could enhance the change statement in the following way:
This would read as follows: given state S1 the player PB1 inserts a black token at position (2,2); this yields a new state S2.
With or without a specified context but with regard to a set of possible change statements it can be — which is the usual case — that there is more than one option what can be changed. Some of the main types of changes are the following ones:
RANDOM
NOT RANDOM, which can be specified as follows:
With PROBABILITIES (classical, quantum probability, …)
DETERMINISTIC
Furthermore, if the causing object is an actor which can adapt structurally or even learn locally then this actor can appear in some time period like a deterministic system, in different collected time periods as an ‘oscillating system’ with different behavior, or even as a random system with changing probabilities. This make the forecast of systems with adaptive and/ or learning systems rather difficult.
Another aspect results from the fact that there can be states either with one actor which can cause more than one action in parallel or a state with multiple actors which can act simultaneously. In both cases the resulting total change has eventually to be ‘filtered’ through some additional rules telling what is ‘possible’ in a state and what not. Thus if in the example of figure 6 both player want to insert a token at position (2,2) simultaneously then either the rules of the game would forbid such a simultaneous action or — like in a computer game — simultaneous actions are allowed but the ‘geometry of a 2-dimensional space’ would not allow that two different tokens are at the same position.
Another aspect of change is the dimension of time. If the time dimension is not explicitly specified then a change from some state S_i to a state S_j does only mark the follow up state S_j as later. There is no specific ‘metric’ of time. If instead a certain ‘clock’ is specified then all changes have to be aligned with this ‘overall clock’. Then one can specify at what ‘point of time t’ the change will begin and at what point of time t*’ the change will be ended. If there is more than one change specified then these different changes can have different timings.
THIRD PERSON VIEW
Up until now the point of view describing a state and the possible changes of states is done in the so-called 3rd-person view: what can a person perceive if it is part of a situation and is looking into the situation. It is explicitly assumed that such a person can perceive only the ‘surface’ of objects, including all kinds of actors. Thus if a driver of a car stears his car in a certain direction than the ‘observing person’ can see what happens, but can not ‘look into’ the driver ‘why’ he is steering in this way or ‘what he is planning next’.
A 3rd-person view is assumed to be the ‘normal mode of observation’ and it is the normal mode of empirical science.
Nevertheless there are situations where one wants to ‘understand’ a bit more ‘what is going on in a system’. Thus a biologist can be interested to understand what mechanisms ‘inside a plant’ are responsible for the growth of a plant or for some kinds of plant-disfunctions. There are similar cases for to understand the behavior of animals and men. For instance it is an interesting question what kinds of ‘processes’ are in an animal available to ‘navigate’ in the environment across distances. Even if the biologist can look ‘into the body’, even ‘into the brain’, the cells as such do not tell a sufficient story. One has to understand the ‘functions’ which are enabled by the billions of cells, these functions are complex relations associated with certain ‘structures’ and certain ‘signals’. For this it is necessary to construct an explicit formal (mathematical) model/ theory representing all the necessary signals and relations which can be used to ‘explain’ the obsrvable behavior and which ‘explains’ the behavior of the billions of cells enabling such a behavior.
In a simpler, ‘relaxed’ kind of modeling one would not take into account the properties and behavior of the ‘real cells’ but one would limit the scope to build a formal model which suffices to explain the oservable behavior.
This kind of approach to set up models of possible ‘internal’ (as such hidden) processes of an actor can extend the 3rd-person view substantially. These models are called in this text ‘actor models (AM)’.
HIDDEN WORLD PROCESSES
In this text all reported 3rd-person observations are called ‘actor story’, independent whether they are done in a pictorial or a textual mode.
As has been pointed out such actor stories are somewhat ‘limited’ in what they can describe.
It is possible to extend such an actor story (AS) by several actor models (AM).
An actor story defines the situations in which an actor can occur. This includes all kinds of stimuli which can trigger the possible senses of the actor as well as all kinds of actions an actor can apply to a situation.
The actor model of such an actor has to enable the actor to handle all these assumed stimuli as well as all these actions in the expected way.
While the actor story can be checked whether it is describing a process in an empirical ‘sound’ way, the actor models are either ‘purely theoretical’ but ‘behavioral sound’ or they are also empirically sound with regard to the body of a biological or a technological system.
A serious challenge is the occurrence of adaptiv or/ and locally learning systems. While the actor story is a finite description of possible states and changes, adaptiv or/ and locally learning systeme can change their behavior while ‘living’ in the actor story. These changes in the behavior can not completely be ‘foreseen’!
COGNITIVE EXPERT PROCESSES
According to the preceding considerations a homo sapiens as a biological system has besides many properties at least a consciousness and the ability to talk and by this to communicate with symbolic languages.
Looking to basic modes of an actor story (AS) one can infer some basic concepts inherently present in the communication.
Without having an explicit model of the internal processes in a homo sapiens system one can infer some basic properties from the communicative acts:
Speaker and hearer presuppose a space within which objects with properties can occur.
Changes can happen which presuppose some timely ordering.
There is a disctinction between concrete things and abstract concepts which correspond to many concrete things.
There is an implicit hierarchy of concepts starting with concrete objects at the ‘root level’ given as occurence in a concrete situation. Other concepts of ‘higher levels’ refer to concepts of lower levels.
There are different kinds of relations between objects on different conceptual levels.
The usage of language expressions presupposes structures which can be associated with the expressions as their ‘meanings’. The mapping between expressions and their meaning has to be learned by each actor separately, but in cooperation with all the other actors, with which the actor wants to share his meanings.
It is assume that all the processes which enable the generation of concepts, concept hierarchies, relations, meaning relations etc. are unconscious! In the consciousness one can use parts of the unconscious structures and processes under strictly limited conditions.
To ‘learn’ dedicated matters and to be ‘critical’ about the quality of what one is learnig requires some disciplin, some learning methods, and a ‘learning-friendly’ environment. There is no guaranteed method of success.
There are lots of unconscious processes which can influence understanding, learning, planning, decisions etc. and which until today are not yet sufficiently cleared up.
Last corrections: 14.February 2019 (add some more keywords; added emphasizes for central words)
Change: 5.May 2019 (adding the the aspect of simulation and gaming; extending the view of the driving actors)
CONTEXT
An overview to the enhanced AAI theory version 2 you can find here. In this post we talk about the blueprint of the whole AAI analysis process. Here I leave out the topic of actor models (AM); the aspect of simulation and gaming is mentioned only shortly. For these topics see other posts.
THE AAI ANALYSIS BLUEPRINT
Blueprint of the whole AAI analysis process including the epistemological assumptions. Not shown here is the whole topic of actor models (AM) and as well simulation.
The Actor-Actor Interaction (AAI)analysis is understood here as part of an embracing systems engineering process (SEP), which starts with the statement of a problem (P) which includes a vision (V) of an improved alternative situation. It has then to be analyzed how such a new improved situation S+ looks like; how one can realize certain tasks (T) in an improved way.
DRIVING ACTORS
The driving actors for such an AAI analysis are at least one stakeholder (STH) which communicates a problem P and an envisioned solution (ES) to an expert (EXPaai) with a sufficient AAI experience. This expert will take the lead in the process of transforming the problem and the envisioned solution into a working solution (WS).
In the classical industrial case the stakeholder can be a group of managers from some company and the expert is also represented by a whole team of experts from different disciplines, including the AAI perspective as leading perspective.
In another case which I will call here the communal case — e.g. a whole city — the stakeholder as well as the experts are members of the communal entity. As in the before mentioned cases there is some commonly accepted problem P combined with a first envisioned solution ES, which shall be analyzed: what is needed to make it working? Can it work at all? What are costs? And many other questions can arise. The challenge to include all relevant experience and knowledge from all participants is at the center of the communication and to transform this available knowledge into some working solution which satisfies all stated requirements for all participants is a central condition for the success of the project.
EPISTEMOLOGY
It has to be taken into account that the driving actors are able to do this job because they have in their bodies brains (BRs) which in turn include some consciousness (CNS). The processes and states beyond the consciousness are here called ‘unconscious‘ and the set of all these unconscious processes is called ‘the Unconsciousness’ (UCNS).
For more details to the cognitive processes see the post to the philosophical framework as well as the post bottom-up process. Both posts shall be integrated into one coherent view in the future.
SEMIOTIC SUBSYSTEM
An important set of substructures of the unconsciousness are those which enable symbolic language systems with so-called expressions (L) on one side and so-called non-expressions (~L) on the other. Embedded in a meaning relation (MNR) does the set of non-expressions ~L function as the meaning (MEAN) of the expressions L, written as a mapping MNR: L <—> ~L. Depending from the involved sensors the expressions L can occur either as acoustic events L_spk, or as visual patternswritten L_txt or visual patterns as pictures L_pict or even in other formats, which will not discussed here. The non-expressions can occur in every format which the brain can handle.
While written (symbolic) expressions L are only associated with the intended meaning through encoded mappings in the brain, the spoken expressions L_spk as well as the pictorial ones L_pict can show some similarities with the intended meaning. Within acoustic expressions one can ‘imitate‘ some sounds which are part of a meaning; even more can the pictorial expressions ‘imitate‘ the visual experience of the intended meaning to a high degree, but clearly not every kind of meaning.
DEFINING THE MAIN POINT OF REFERENCE
Because the space of possible problems and visions it nearly infinite large one has to define for a certain process the problem of the actual process together with the vision of a ‘better state of the affairs’. This is realized by a description of he problem in a problem document D_p as well as in a vision statement D_v. Because usually a vision is not without a given context one has to add all the constraints (C) which have to be taken into account for the possible solution. Examples of constraints are ‘non-functional requirements’ (NFRs) like “safety” or “real time” or “without barriers” (for handicapped people). Part of the non-functional requirements are also definitions of win-lose states as part of a game.
AAI ANALYSIS – BASIC PROCEDURE
If the AAI check has been successful and there is at least one task T to be done in an assumed environment ENV and there are at least one executing actor A_exec in this task as well as an assisting actor A_ass then the AAI analysis can start.
ACTOR STORY (AS)
The main task is to elaborate a complete description of a process which includes a start state S* and a goal state S+, where the participating executive actors A_exec can reach the goal state S+ by doing some actions. While the imagined process p_v is a virtual (= cognitive/ mental) model of an intended real process p_e, this intended virtual model p_e can only be communicated by a symbolic expressions L embedded in a meaning relation. Thus the elaboration/ construction of the intended process will be realized by using appropriate expressions L embedded in a meaning relation. This can be understood as a basic mapping of sensor based perceptions of the supposed real world into some abstract virtual structures automatically (unconsciously) computed by the brain. A special kind of this mapping is the case of measurement.
In this text especially three types of symbolic expressions L will be used: (i) pictorial expressions L_pict, (ii) textual expressions of a natural language L_txt, and (iii) textual expressions of a mathematical language L_math. The meaning part of these symbolic expressions as well as the expressions itself will be called here an actor story (AS) with the different modes pictorial AS (PAS), textual AS (TAS), as well as mathematical AS (MAS).
The basic elements of an actor story (AS) are states which represent sets of facts. A fact is an expression of some defined language L which can be decided as being true in a real situation or not (the past and the future are special cases for such truth clarifications). Facts can be identified as actors which can act by their own. The transformation from one state to a follow up state has to be described with sets of change rules. The combination of states and change rules defines mathematically a directedgraph (G).
Based on such a graph it is possible to derive an automaton (A) which can be used as a simulator. A simulator allows simulations. A concrete simulation takes a start state S0 as the actual state S* and computes with the aid of the change rules one follow up state S1. This follow up state becomes then the new actual state S*. Thus the simulation constitutes a continuous process which generally can be infinite. To make the simulation finite one has to define some stop criteria (C*). A simulation can be passive without any interruption or interactive. The interactive mode allows different external actors to select certain real values for the available variables of the actual state.
If in the problem definition certain win-lose states have been defined then one can turn an interactive simulation into a game where the external actors can try to manipulate the process in a way as to reach one of the defined win-states. As soon as someone (which can be a team) has reached a win-state the responsible actor (or team) has won. Such games can be repeated to allow accumulation of wins(or loses).
Gaming allows a far better experience of the advantages or disadvantages of some actor story as a rather lose simulation. Therefore the probability to detect aspects of an actor story with their given constraints is by gaming quite high and increases the probability to improve the whole concept.
Based on an actor story with a simulator it is possible to increase the cognitive power of exploring the future even more. There exists the possibility to define an oracle algorithm as well as different kinds of intelligent algorithms to support the human actor further. This has to be described in other posts.
TAR AND AAR
If the actor story is completed (in a certain version v_i) then one can extract from the story the input-output profiles of every participating actor. This list represents the task-induced actor requirements (TAR). If one is looking for concrete real persons for doing the job of an executing actor the TAR can be used as a benchmark for assessing candidates for this job. The profiles of the real persons are called here actor-actor induced requirements (AAR), that is the real profile compared with the ideal profile of the TAR. If the ‘distance’ between AAR and TAR is below some threshold then the candidate has either to be rejected or one can offer some training to improve his AAR; the other option is to change the conditions of the TAR in a way that the TAR is more closer to the AARs.
The TAR is valid for the executive actors as well as for the assisting actors A_ass.
CONSTRAINTS CHECK
If the actor story has in some version V_i a certain completion one has to check whether the different constraints which accompany the vision document are satisfied through the story: AS_vi |- C.
Such an evaluation is only possible if the constraints can be interpreted with regard to the actor story AS in version vi in a way, that the constraints can be decided.
For many constraints it can happen that the constraints can not or not completely be decided on the level of the actor story but only in a laterphase of the systems engineering process, when the actor story will be implemented in software and hardware.
MEASURING OF USABILITY
Using the actor story as a benchmark one can test the quality of the usability of the whole process by doing usability tests.
An overview of the enhanced AAI theory version 2 you can find here. In this post you can find the unified references from the different posts.
REFERENCES
ISO/IEC 25062:2006(E)
Joseph S. Dumas and Jean E. Fox. Usability testing: Current practice and future directions. chapter 57, pp.1129 – 1149, in J.A. Jacko and A. Sears, editors, The Human-Computer Interaction Handbook. Fundamentals, Evolving Technologies, and Emerging Applications. 2nd edition, 2008
S. Lauesen. User Interface Design. A software Engineering Perspective.
Pearson – Addison Wesley, London et al., 2005
An overview of the enhanced AAI theory version 2 you can find here. In this post we talk about the tenth chapter dealing with Measuring Usability
MEASURING USABILITY
As has been delineated in the post “Usability and Usefulness” statements about the quality of the usability of some assisting actor are based on some kinds of measurement: mapping some target (here the interactions of an executive actor with some assistive actor) into some predefined norm (e.g. ‘number of errors’, ‘time needed for completion’, …). These remarks are here embedded in a larger perspective following Dumas and Fox (2008).
Overview of Usability Testing following the article of Dumas & Fox (2008), with some new AAI specific terminology
From the three main types of usability testing with regard to the position in the life-cycle of a system we focus here primarily on the usability testing as part of the analysis phase where the developers want to get direct feedback for the concepts embedded in an actor story. Depending from this feedback the actor story and its related models can become modified and this can result in a modified exploratory mock-up for a new test. The challenge is not to be ‘complete’ in finding ‘disturbing’ factors during an interaction but to increase the probability to detect possible disturbing factors by facing the symbolically represented concepts of the actor story with a sample of real world actors. Experiments point to the number of 5-10 test persons which seem to be sufficient to detect the most severe disturbing factors of the concepts.
Usability testing procedure according to Lauesen (2005), adapted to the AAI paradigm
A good description of usability testing can be found in the book Lauesen (2005), especially chapters 1 +13. According to this one can infer the following basic schema for a usability test:
One needs 5 – 10 test persons whose input-output profile (AAR) comes close to the profile (TAR) required by the actor story.
One needs a mock-up of the assistive actor; this mock-up should correspond ‘sufficiently well’ with the input-output profile (TAR) required by the actor story. In the simplest case one has a ‘paper model’, whose sheets can be changed on demand.
One needs a facilitator who is receiving the test person, introduces the test person into the task (orally and/ or by a short document (less than a page)), then accompanies the test without interacting further with the test person until the end of the test. The end is either reached by completing the task or by reaching the end of a predefined duration time.
After the test person has finished the test a debriefing happens by interrogating the test person about his/ her subjective feelings about the test. Because interviews are always very fuzzy and not very reliable one should keep this interrogation simple, short, and associated with concrete points. One strategy could be to ask the test person first about the general feeling: Was it ‘very good’, ‘good’, ‘OK’, ‘undefined’, ‘not OK’, ‘bad’, ‘very bad’ (+3 … 0 … -3). If the stated feeling is announced then one can ask back which kinds of circumstances caused these feelings.
During the test at least two observers are observing the behavior of the test person. The observer are using as their ‘norm’ the actor story which tells what ‘should happen in the ideal case’. If a test person is deviating from the actor story this will be noted as a ‘deviation of kind X’, and this counts as an error. Because an actor story in the mathematical format represents a graph it is simple to quantify the behavior of the test person with regard to how many nodes of a solution path have been positively passed. This gives a count for the percentage of how much has been done. Thus the observer can deliver data about at least the ‘percentage of task completion’, ‘the number (and kind) of errors by deviations’, and ‘the processing time’. The advantage of having the actor story as a norm is that all observers will use the same ‘observation categories’.
From the debriefing one gets data about the ‘good/ bad’ feelings on a scale, and some hints what could have caused the reported feelings.
STANDARDS – CIF (Common Industry Format)
There are many standards around describing different aspects of usability testing. Although standards can help in practice from the point of research standards are not only good, they can hinder creative alternative approaches. Nevertheless I myself are looking to standards to check for some possible ‘references’. One standard I am using very often is the “Common Industry Format (CIF)” for usability reporting. It is an ISO standard (ISO/IEC 25062:2006) since 2006. CIF describes a method for reporting the findings of usability tests that collect quantitative measurements of user performance. CIF does not describe how to carry out a usability test, but it does require that the test include measurements of the application’s effectiveness and efficiency as well as a measure of the users’ satisfaction. These are the three elements that define the concept of usability.
Applied to the AAI paradigm these terms are fitting well.
Effectiveness in CIF is targeting the accuracy and completeness with which users achieve their goal. Because the actor story in AAI his represented as a graph where the individual paths represents a way to approach a defined goal one can measure directly the accuracy by comparing the ‘observed path’ in a test and the ‘intended ideal path’ in the actor story. In the same way one can compute the completeness by comparing the observed path and the intended ideal path of the actor story.
Efficiency in CIF covers the resources expended to achieve the goals. A simple and direct measure is the measuring of the time needed.
Users’ satisfaction in CIF means ‘freedom from discomfort’ and ‘positive attitudes towards the use of the product‘. These are ‘subjective feelings’ which cannot directly be observed. Only ‘indirect’ measures are possible based on interrogations (or interactions with certain tasks) which inherently are fuzzy and not very reliable. One possibility how to interrogate is mentioned above.
Because the term usability in CIF is defined by the before mentioned terms of effectiveness, efficiency as well as users’ satisfaction, which in turn can be measured in many different ways the meaning of ‘usability’ is still a bit vague.
DYNAMIC ACTORS – CHANGING CONTEXTS
With regard to the AAI paradigm one has further to mention that the possibility of adaptive, learning systems embedded in dynamic, changing environments requires for a new type of usability testing. Because learning actors change by every exercise one should run a test several times to observe how the dynamic learning rates of an actor are developing in time. In such a dynamic framework a system would only be ‘badly usable‘ when the learning curves of the actors can not approach a certain threshold after a defined ‘typical learning time’. And, moreover, there could be additional effects occurring only in a long-term usage and observation, which can not be measured in a single test.
REFERENCES
ISO/IEC 25062:2006(E)
Joseph S. Dumas and Jean E. Fox. Usability testing: Current practice and future directions. chapter 57, pp.1129 – 1149, in J.A. Jacko and A. Sears, editors, The Human-Computer Interaction Handbook. Fundamentals, Evolving Technologies, and Emerging Applications. 2nd edition, 2008
S. Lauesen. User Interface Design. A software Engineering Perspective.
Pearson – Addison Wesley, London et al., 2005
This text has to be reviewed again on account of the new aspect of gaming as discussed in the post Engineering and Society.
CONTEXT
An overview of the enhanced AAI theory version 2 you can find here. In this post we talk about the sixth chapter dealing with usability and usefulness.
USABILITY AND USEFULNESS
In the AAI paradigm the concept of usability is seen as a sub-topic of the more broader concept of usefulness. Furthermore Usefulness as well as usability are understood as measurements comparing some target with some presupposed norm.
Example: If someone wants to buy a product A whose prize fits well with the available budget and this product A shows only an average usability then the product is probably ‘more useful’ for the buyer than another product B which does not fit with the budget although it has a better usability. A conflict can arise if the weaker value of the usability of product A causes during the usage of product A ‘bad effects’ onto the user of product A which in turn produce additional negative costs which enhance the original ‘nice price’ to a degree where the product A becomes finally ‘more costly’ than product B.
Therefore the concept usefulness will be defined independently from the concept usability and depends completely from the person or company who is searching for the solution of a problem. The concept of usability depends directly on the real structure of an actor, a biological one or a non-biological one. Thus independent of the definition of the actual usefulness the given structure of an actor implies certain capabilities with regard to input, output as well as to internal processing. Therefore if an X seems to be highly useful for someone and to get X needs a certain actor story to become realized with certain actors then it can matter whether this process includes a ‘good usability’ for the participating actors or not.
In the AAI paradigm both concepts usefulness as well as usability will be analyzed to provide a chance to check the contributions of both concepts in some predefined duration of usage. This allows the analysis of the sustainability of the wanted usefulness restricted to usability as a parameter. There can be even more parameters included in the evaluation of the actor story to enhance the scope of sustainability. Depending from the definition of the concept of resilience one can interpret the concept of sustainability used in this AAI paradigm as compatible with the resilience concept too.
MEASUREMENT
To speak about ‘usefulness’, ‘usability’, ‘sustainability’ (or ‘resilience’) requires some kind of a scale of values with an ordering relation R allowing to state about some values x,y whether R(x,y) or R(y,x) or EQUAL(x,y). The values used in the scale have to be generated by some defined process P which is understood as a measurement process M which basically compares some target X with some predefined norm N and gives as a result a pair (v,N) telling a number v associated with the applied norm N. Written: M : X x N —> V x N.
A measurement procedure M must be transparent and repeatable in the sense that the repeated application of the measurement procedure M will generate the same results than before. Associated with the measurement procedure there can exist many additional parameters like ‘location’, ‘time’, ‘temperature’, ‘humidity’, ‘used technologies’, etc.
Because there exist targets X which are not static it can be a problem when and how often one has to measure these targets to get some reliable value. And this problem becomes even worse if the target includes adaptive systems which are changing constantly like in the case of biological systems.
All biological systems have some degree of learnability. Thus if a human actor is acting as part of an actor story the human actor will learn every time he is working through the process. Thus making errors during his first run of the process does not imply that he will repeat these errors the next time. Usually one can observe a learning curve associated with n-many runs which show — mostly — a decrease in errors, a decrease in processing time, and — in general — a change of all parameters, which can be measured. Thus a certain actor story can receive a good usability value after a defined number of usages. But there are other possible subjective parameters like satisfaction, being excited, being interested and the like which can change in the opposite direction, because to become well adapted to the process can be boring which in turn can lead to less concentrations with many different negative consequences.
An overview to the enhanced AAI theory version 2 you can find here. In this post we talk about the special topic how the actor story (AS) can be used for a modeling of the real world (RW).
AS AND REAL WORLD MODELING
In the preceding post you find a rough description how an actor story can be generated challenged by a problem P. Here I shall address the question, how this procedure can be used to model certain aspects of the real world and not some abstract ideas only.
There are two main elements of the actor story which can be related to the real world: (i) The start state of the actor story and the list of possible change expressions.
FACTS
A start state is a finite set of facts which in turn are — in the case of the mathematical language — constituted by names of objects associated with properties or relations. Primarily the possible meaning of these expressions is located in the cognitive structures of the actors. These cognitive structures are as such not empirical entities and are partially available in a state called consciousness. If some element of meaning is conscious andsimultaneously part of the inter-subjective space between different actors in a way that all participating actors can perceive these elements, then these elements are called empirical by everyday experience, if these facts can be decided between the participants of the situation. If there exist further explicit measurement procedures associating an inter-subjective property with inter-subjective measurement data then these elements are called genuineempirical data.
Thus the collection of facts constituting a state of an actor story can be realized as a set of empirical facts, at least in the format of empirical by everyday experience.
CHANGES
While a state represents only static facts, one needs an additional element to be able to model the dynamic aspect of the real world. This is realized by change expressions X.
The general idea of a change is that at least one fact f of an actual state (= NOW), is changed either by complete disappearance or by changing some of its properties or by the creation of a new fact f1. An object called ‘B1’ with the property being ‘red’ — written as ‘RED(B1)’ — perhaps changes its property from being ‘red’ to become ‘blue’ — written as ‘BLUE(B1)’ –. Then the set of facts of the actual state S0= {RED(B1)} will change to a successor state S1={BLUE(B1)}. In this case the old fact ‘RED(B1)’ has been deleted and the new fact ‘BLUE(B1)’ has been created. Another example: the object ‘B1’ has also a ‘weight’ measured in kg which changes too. Then the actual state was S0={RED(B1), WEIGHT(B1,kg,2.4)} and this state changed to the successor state S1= {BLUE(B1), WEIGHT(B1,kg,3.4)}.
The possible cause of a change can be either an object or the ‘whole state‘ representing the world.
The mapping from a given state s into a successor state s’ by subtracting facts f- and joining facts f+ is here called an action: S –> S-(f-) u (f+) or action(s) = s’ = s-(f-) u (f+) with s , s’ in S
Because an action has an actor as a carrier one can write action: S x A –> S-(f-) u (f+) or action_a(s) = s’.
The defining properties of such an action are given in the sets of facts to be deleted — written as ‘d:{f-}’ — and the sets of facts to be created — written ‘c:{f+}’ –.
A full change expression amounts then to the following format: <s,s’, obj-name, action-name, d:{…}, c:{…}>.
But this is not yet the whole story. A change can be deterministic or indeterministic.
The deterministic change is cause by a deterministic actor or by a deterministic world.
The indeterministic change can have several formats:e.g. classical probability or quantum-like probability or the an actor as cause, whose behavior is not completely deterministic.
Additionally there can be interactions between different objects which can cause a change and these changes happen in parallel, simultaneously. Depending from the assumed environment (= world) and some laws describing the behavior of this world it can happen, that different local actions can hinder each other or change the effect of the changes.
Independent of the different kinds of changes it can be required that all used change-expressions should be of that kind that one can state that they are empirical by everyday experience.
TIME
And there is even more to tell. A change has in everyday life a duration measured with certain time units generated by a technical device called a clock.
To improve the empirical precision of change expressions one has to add the duration of the change between the actual state s and the final state s’ showing all the deletes (f-) and creates (f+) which are caused by this change-expression. This can only be done if a standard clock is included in the facts represented by the actual time stamp of this clock. Thus with regard to such a standard time one can realize a change with duration (t,t’) exactly in coherence with the standard time. A special case is given when a change-expression describes the effects of its actions in a distributed manner by giving more than one time point (t,t1, …, tn) and associating different deletes and creates with different points of time. Those distributed effects can make an actor story rather complex and difficult to understand by human brains.
An overview to the enhanced AAI theory version 2 you can find here. In this post we talk about the special topic how to generate an actor story.
GENERATING AN AS
Outline of the process how to generate an AS
Until now it has been described which final format an actor story (AS) should have. Three different modes (textual, pictorial, mathematical) have been distinguished. The epistemology of these expressions has been outlined to shed some light on the underlying cognitive processes enabling such a story.
Now I describe a possible process which has the capacity to generate an AS.
As the introductory figure shows it is assumed here that there is a collection of citizens and experts which offer their individual knowledge, experiences, and skills to ‘put them on the table’ challenged by a given problem P.
This knowledge is in the beginning not structured. The first step in the direction of an AS is to analyze the different contributions in a way which shows distinguishable elements with properties and relations. Such a set of first ‘objects’ and ‘relations’ characterizes a set of facts which define a ‘situation’ or a ‘state’ as a collection of ‘facts’. Such a situation/ state can also be understood as a first simple ‘model‘ as response to a given problem. A model is as such ‘static‘; it describes what ‘is’ at a certain point of ‘time’.
In a next step the group has to identify possible ‘changes‘ which can be associated with t least one fact. There can be many possible changes which can need different durations to come into effect. Furthermore they can be ‘alternatively’ or in ‘parallel’. Combining a situation (model) with possible changes allows the application of the actual situation which generates a — or many — ‘successors’ to the actual situation. A process starts which we call usually ‘simulation‘.
If one allows the interaction between real actors with the simulation by mapping a real actor to one of the actors ‘inside the simulation’ one is turning the simulation into an ‘interactive simulation‘ which represents basically a ‘computer game‘ (short: ‘egame‘).
One can use interactive simulations e.g. to (i) learn about the dynamics of a model, to (ii) test the assumptions of a model, to (iii) test the knowledge and skills of the real actors.
Making new experiences with a (interactive) simulation allows a continuous improvement of the model and its change rules.
Additionally one can include more citizens and experts into this process, using available knowledge from databases and libraries etc.
An overview to the enhanced AAI theory version 2 you can find here. In this post we talk about the special topic of the virtual meaning and the actor story.
VIRTUAL MEANING AND ACTOR STORY
In a textual actor story (TAS) using the symbolic expressions L0 of some everyday language one can describe a state with a finite set of facts which should be decidable ‘in principle’ when related to the supposed external empirical environment of the problem P. Thus the constraint of ‘operational decidability‘ with regard to an empirical external environment imposes some constraint of the kinds of symbolic expressions which can be used. If there is more than only one state (which is the usual case) then one has to provide a list of ‘possible changes‘. Each change is described with a symbolic expression L0x. The content of a change is at least one fact which will change between the ‘given’ state and the ‘succeeding’ state. Thus the virtual meaning of an actor must enable the actor to distinguish between a ‘given state’ q_now and a possible ‘succeeding state’ q_next. There can be more than one possible change with regard to a given state q_now. Thus a textual actor story (TAS) is a set of states connected by changes, all represented as finite collections of symbolic expressions.
In a pictorial actor story (PAS) using the graphical expressions Lg of some everyday pictorial langue one can describe a state with a finite set of facts realized as pictures of objects, properties as well as relations between these objects. The graphs of the objects can be enhanced by graphs including symbolic expressions L0 of some everyday language. Again it should be decidable ‘in principle’ whether these pictorial facts can be related to the suppose external empirical environment of the problem P. Thus the constraint of ‘operational decidability’ with regard to an empirical external environment imposes some constraint of the kinds of symbolic expressions which can be used. If there is more than only one state (which is the usual case) then one has to provide a list of ‘possible changes’. Each change is described with an expression Lgx. The content of a change is at least one fact which will change between the ‘given’ state and an ‘succeeding’ state. Thus the virtual meaning of an actor must enable the actor to distinguish between a ‘given state’ q_now and a possible ‘succeeding state’ q_next. There can be more than one possible change with regard to a given state q_now. Thus a pictorial actor story (TAS) is a set of states connected by changes, all represented as finite collections of graphical expressions.
In the case of a mathematical actor story (MAS) one has to distinguish two cases: (i) a complete formal description or (ii) a graphical presentation enhanced with symbolic expressions.
In case (i) it is similar to the textual mode but replacing the symbolic expressions L0 of some everyday langue with the symbolic expressions Lm of some mathematical language. In this book we are using predicate logic syntax with a new semantics. In case (ii) one describes the actor story as a mathematical directed graph. The nodes (vertices) of the graph are understood as ‘states’ and the arrows connecting the nodes are understood as changes. A node representing a state can be attached to a finite set of facts, where a fact is a symbolic expression Lm representing objects, properties as well as relations between these objects. Again it should be decidable ‘in principle’ whether these facts can be related to the suppose external empirical environment of the problem P. Thus the constraint of ‘operational decidability’ with regard to an empirical external environment imposes some constraint of the kinds of symbolic expressions which can be used. If there is more than only one state (which is the usual case) then one has to use arrows which are labeled by symbolic change expressions Lmx. The content of a change is at least one fact which will change between the ‘given’ state and an ‘succeeding’ state. Thus the virtual meaning of an actor must enable the actor to distinguish between a ‘given state’ q_now and a possible ‘succeeding state’ q_next. There can be more than one possible change with regard to a given state q_now.
If the complete actor story is given, then there is no need for the additional change expressions LX because one can infer the changes from the pairs of the succeeding states directly. But if one wants to ‘generate’ an actor story beginning with the start state then one needs the list of change expressions.
An overview to the enhanced AAI theory version 2 you can find here. In this post we talk about the special topic of the meaning of (symbolic) expressions.
MEANING: REAL AND VIRTUAL
In semiotic terminology the ‘meaning‘ of a symbolic expression corresponds to the image of the mapping from symbolic expressions (L) into something else (non-L). This mapping is located in that system which is using this mapping. We can call this system a ‘semiotic system‘.
For the generation of an actor story we assume that the AAI experts as well as all the other actors collaborating with the AAI actors are input-output systems with changeable internal states (IS) as well as a behavior function (phi), written as phi: I x IS —> IS x O.
These actors are embedded in an empirical environment (ENV) which is continuously changing.
Parts of the environment can interact with the actors by inducing physical state-changes in parts of the actors (Stimuli (S), Input, (I)) as well as receiving physical responses from the actors (Responses (R), output (O)) which change parts of the environmental states.
Interpreting these actors as ‘semiotic systems’ implies that the actors can receive as input symbolic expressions (L) as well as non-symbolic events and they can output symbolic expressions (L) as well as some non-symbolic events (non-L). Furthermore the mapping from symbolic expressions into something else is assumed to happen ‘inside‘ the system.
From a 3rd-person view one can distinguish the empirical environment external to the actor as well as the empirical states ‘inside’ the system (typically investigated by Physiology with many sub-disciplines).
The internal states on the cellular level have a small subset called ‘brain’ (less than 1% of all cellular elements). A subset of the brain cells is enabling what in a 1st person view is called ‘consciousness‘. The ‘content’ of the consciousness consists of ‘phenomena‘ which are not ’empirical’ in the usual sense of ’empirical’. Using the consciousness as point of reference everything else of the actor which is not part of the conscious is ‘not conscious‘ or ‘unconscious‘. The ‘unconsciousness‘ is then the set of all non-conscious states of the actor (which means in the biological case of human sapiens more than 99% of all body states).
As empirical sciences have revealed there exist functional relations between empirical states of the external environment (S_emp) and the set of externally caused internal unconscious input states of the actor (IS_emp_uc).
The internally caused unconscious input states (IS_emp_uc) are further processed and mapped in a variety of internal unconscious states (IS_emp_uc_abstr), which are more ‘general’ as the original input states. Thus subsets of internally cause unconscious internal states IS_emp_uc can be elements of the more abstract internal states IS_emp_uc_abstr.
These internal unconscious states are part of ‘networks‘ and parts of different kinds of ‘hierarchies‘.
There are many different kinds of internal operations working on these internal structures including the input states.
Parts of the internal structures can function as ‘meaning‘ (M) for other parts of internal structures which function as ‘symbolic expressions‘ (L). Symbolic expressions together with the meaning constituting elements can be used from an actor (seen as a semiotic system) as a ‘symbolic language‘ whose observable part are the ‘symbols’ (written, spoken, gestures, …) and whose non-observable part is the mapping relation (encoding) from symbols into the internal meaning elements.
The primary meaning of a language is therefore a ‘virtual world of states inside the actor‘ compared to the ‘external empirical world‘. Parts of the virtual meaning world can correspond to parts of the empirical world outside. To control such an important relationship one needs commonly defined empirical measurement procedures (MP) which are producing external empirical events which can be repeatedly perceived by a population of actors, which can compare these processes and events with their 1st person conscious phenomena (PH). If it is possible for an actor (an observer) to identify those phenomena which correspond to the external measurement events than it is possible (in principle) to define that subset of Phenomena (PH_emp) which are phenomena but are correlated with events in the external empirical world. Usually those phenomena which correspond to empirical events external PH_emp are a true subset of the set of all possible Phenomena, written as PH_emp ⊂ PH.
While the empirical phenomena PH_emp are ‘concrete‘ phenomena are the non-empirical phenomena PH_abs = PH-PH_emp ‘abstract‘ in the sense that an empirical phenomenon p_emp can be an element of a non-empirical phenomenon p_abs if p_emp is not new.
While the virtual meaning of a symbolic language is realized by abstract structures which can be ‘cited’ in the consciousness as p_abs, the empirical meaning instead occurs as concrete structures which can be ‘cited’ by the consciousness.
All meaning elements can occur as part of a virtual spatial structure (VR) and as part of a virtual timely structure (VT).
There is no 1-to-1 mapping from the spatial and timely structures of the external empirical world into the virtual internal world of meanings.
If it is possible to correlate virtual meaning structures with external empirical structures we call this ’empirical soundness’ or ’empirical truth’.
An overview to the enhanced AAI theory version 2 you can find here. In this post we talk about the generation of the actor story (AS).
ACTOR STORY
To get from the problem P to an improved configuration S measured by some expectation E needs a process characterized by a set of necessary states Q which are connected by necessary changes X. Such a process can be described with the aid of an actor story AS.
The target of an actor story (AS) is a full specification of all identified necessary tasks T which lead from a start state q* to a goal state q+, including all possible and necessary changes X between the different states M.
A state is here considered as a finite set of facts (F) which are structured as an expression from some language L distinguishing names of objects (like ‘D1’, ‘Un1’, …) as well as properties of objects (like ‘being open’, ‘being green’, …) or relations between objects (like ‘the user stands before the door’). There can also e a ‘negation’ like ‘the door is not open’. Thus a collection of facts like ‘There is a door D1’ and ‘The door D1 is open’ can represent a state.
Changes from one state q to another successor state q’ are described by the object whose action deletes previous facts or creates new facts.
In this approach at least three different modes of an actor story will be distinguished:
A textual mode generating a Textual Actor Story (TAS): In a textual mode a text in some everyday language (e.g. in English) describes the states and changes in plain English. Because in the case of a written text the meaning of the symbols is hidden in the heads of the writers it can be of help to parallelize the written text with the pictorial mode.
A pictorial mode generating a Pictorial Actor Story (PAS). In a pictorial mode the drawings represent the main objects with their properties and relations in an explicit visual way (like a Comic Strip). The drawings can be enhanced by fragments of texts.
A mathematical mode generating a Mathematical Actor Story (MAS): this can be done either (i) by a pictorial graph with nodes and edges as arrows associated with formal expressions or (ii) by a complete formal structure without any pictorial elements.
For every mode it has to be shown how an AAI expert can generate an actor story out of the virtual cognitive world of his brain and how it is possible to decide the empirical soundness of the actor story.
An overview to the enhanced AAI theory version 2 you can find here. In this post we talk about the fourth chapter dealing with the epistemology of actors within an AAI analysis process.
EPISTEMOLOGY AND THE EMPIRICAL SCIENCES
Epistemology is a sub-discipline of general philosophy. While a special discipline in empirical science is defined by a certain sub-set of the real world RW by empirical measurement methods generating empirical data which can be interpreted by a formalized theory, philosophy is not restricted to a sub-field of the real world. This is important because an empirical discipline has no methods to define itself. Chemistry e.g. can define by which kinds of measurement it is gaining empirical data and it can offer different kinds of formal theories to interpret these data including inferences to forecast certain reactions given certain configurations of matters, but chemistry is not able to explain the way how a chemist is thinking, how the language works which a chemist is using etc. Thus empirical science presupposes a general framework of bodies, sensors, brains, languages etc. to be able to do a very specialized — but as such highly important — job. One can define ‘philosophy’ then as that kind of activity which tries to clarify all these conditions which are necessary to do science as well as how cognition works in the general case.
Given this one can imagine that philosophy is in principle a nearly ‘infinite’ task. To get not lost in this conceptual infinity it is recommended to start with concrete processes of communications which are oriented to generate those kinds of texts which can be shown as ‘related to parts of the empirical world’ in a decidable way. This kind of texts is here called ’empirically sound’ or ’empirically true’. It is to suppose that there will be texts for which it seems to be clear that they are empirically sound, others will appear ‘fuzzy’ for such a criterion, others even will appear without any direct relation to empirical soundness.
In empirical sciences one is using so-called empirical measurement procedures as benchmarks to decided whether one has empirical data or not, and it is commonly assumed that every ‘normal observer’ can use these data as every other ‘normal observer’. But because individual, single data have nearly no meaning on their own one needs relations, sets of relations (models) and even more complete theories, to integrate the data in a context, which allows some interpretation and some inferences for forecasting. But these relations, models, or theories can not directly be inferred from the real world. They have to be created by the observers as ‘working hypotheses’ which can fit with the data or not. And these constructions are grounded in highly complex cognitive processes which follow their own built-in rules and which are mostly not conscious. ‘Cognitive processes’ in biological systems, especially in human person, are completely generated by a brain and constitute therefore a ‘virtual world’ on their own. This cognitive virtual world is not the result of a 1-to-1 mapping from the real world into the brain states. This becomes important in that moment where the brain is mapping this virtual cognitive world into some symbolic language L. While the symbols of a language (sounds or written signs or …) as such have no meaning the brain enables a ‘coding’, a ‘mapping’ from symbolic expressions into different states of the brain. In the light’ of such encodings the symbolic expressions have some meaning. Besides the fact that different observers can have different encodings it is always an open question whether the encoded meaning of the virtual cognitive space has something to do with some part of the empirical reality. Empirical data generated by empirical measurement procedures can help to coordinate the virtual cognitive states of different observers with each other, but this coordination is not an automatic process. Empirically sound language expressions are difficult to get and therefore of a high value for the survival of mankind. To generate empirically sound formal theories is even more demanding and until today there exists no commonly accepted concept of the right format of an empirically sound theory. In an era which calls itself ‘scientific’ this is a very strange fact.
EPISTEMOLOGY OF THE AAI-EXPERTS
Applying these general considerations to the AAI experts trying to construct an actor story to describe at least one possible path from a start state to a goal state, one can pick up the different languages the AAI experts are using and asking back under which conditions these languages have some ‘meaning’ and under which conditions these meanings can be called ’empirically sound’?
In this book three different ‘modes’ of an actor story will be distinguished:
A textual mode using some ordinary everyday language, thus using spoken language (stored in an audio file) or written language as a text.
A pictorial mode using a ‘language of pictures’, possibly enhanced by fragments of texts.
A mathematical mode using graphical presentations of ‘graphs’ enhanced by symbolic expressions (text) and symbolic expressions only.
For every mode it has to be shown how an AAI expert can generate an actor story out of the virtual cognitive world of his brain and how it is possible to decided the empirical soundness of the actor story.
Integrating Engineering and the Human Factor (info@uffmm.org) eJournal uffmm.org ISSN 2567-6458
