Within the AAI paradigm the following steps will usually be distinguished:
A given problem and a wanted solution.
An assumed context and intended executing and assisting actors.
Assumed non-functional requirements (NFRs).
An actor story (AS) describing at least one task including all the functional requirements.
An usability test, often enhanced with passive or interactive simulations.
An evaluation of the test.
Some repeated improvements.
With these elements one can analyze and design the behavior surface of an assistive actor which can approach the requirements of the stakeholder.
SIMULATION AND GAMING
Comparing these elements with a (computer) game then one can detect immediately that a game characteristically allows to win or to lose. The possible win-states or lose-states stop a game. Often the winning state includes additionally some measures how ‘strong’ or how ‘big’ someone has won or lost a game.
Thus in a game one has besides the rules of the game R which determine what is allowed to do in a game some set of valuelables V which indicate some property, some object, some state as associated with some value v, optionally associated further with some numbers to quantify the value between a maximum and a minimum.
In most board games you will reach an end state where you are the winner or the loser independent of some value. In other games one plays as often as necessary to reach some accumulated value which gives a measure who is better than someone else.
Doing AAI analysis as part of engineering it is usually sufficient to develop an assistive actor with a behavior surface which satisfies all requirements and some basic needs of the executive actors (the classical users).
But this newly developed product (the assistive actor for certain tasks) will be part of a social situation with different human actors. In these social situations there are often more requirements, demands, emotions around than only the original design criteria for the technical product.
For some people the aesthetic properties of a technical product can be important or some other cultural code which associates the technical product with these cultural codes making it precious or not.
Furthermore there can be whole processes within which a product can be used or not, making it precious or not.
COLLECTIVE INTELLIGENCE AND AUTOPOIETIC GAMES
In the case of simulations one has already from the beginning a special context given by the experience and the knowledge of the executive actors. In some cases this knowledge is assumed to be stable or closed. Therefore there is no need to think of the assistive actor as a tool which has not only to support the fulfilling of a defined task but additionally to support the development of the knowledge and experience of the executive actor further. But there are social situations in a city, in an institution, in learning in general where the assumed knowledge and experience is neither complete nor stable. On the contrary in these situations there is a strong need to develop the assumed knowledge further and do this as a joined effort to improve the collective intelligence collectively.
If one sees the models and rules underlying a simulation as a kind of a representation of the assumed collective knowledge then a simulation can help to visualize this knowledge, make it an experience, explore its different consequences. And as far as the executive actors are writing the rules of change by themselves, they understand the simulation and they can change the rules to understand better, what can improve the process and possible goal states. This kind of collective development of models and rules as well as testing can be called autopoietic because the executing actors have two roles:(i) following some rules (which they have defined by themselves) they explore what will happen, when one adheres to these rules; (ii) changing the rules to change the possible outcomes.
This procedure establishes some kind of collective learning within an autopoietic process.
If one enriches this setting with explicit goal states, states of assumed advantages, then one can look at this collective learning as a serious pattern of collective learning by autopoietic games.
For many context like cities, educational institutions, and even companies this kind of collective learning by autopoietic games can be a very productive way to develop the collective intelligence of many people at the same time gaining knowledge by having some exciting fun.
Autopoietic gaming as support for collective learning processes
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 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 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.
An overview to the enhanced AAI theory version 2 you can find here. In this post we talk about two different strategies how to proceed in the AAI analysis.
TOP-DOWN OR BOTTOM-UP?
The elaboration of an actor story AS happens generally during a process driven by some actors, which communicate with each other and the environment. This can be done in various ways. Here we consider two main cases:
Top-down: There exists a group of experts EXPs which will analyze a possible solution, will test these, and then will propose these as a solution for others. During this process they mainly communicate only with the stakeholder of the problem (and probably with experts from other departments).
Bottom-up: There exists a group of experts EXPs too but additionally there exists a group of customers CTMs which are also the stakeholder of the process and which will be guided by the experts to use their own experience to find a possible solution.
In reality there can be many forms of collaboration which are mixing these two idealized cases. The top-down paradigm is very common although it produces many problems, especially in communal projects. A bottom-up process including the topic of ‘participation’ in communities and cities is today highly demanded, but not well specified and not a common practice.
In this book the bottom-up paradigm will be discussed explicitly. This requires that the AAI experts collaborate from the beginning with a group of common users from the application domain. To do this they will (i) extract the knowledge which is distributed in the different users, then (ii) they will start some modeling from these different facts to (iii) enable some basic simulations. These simple simulations (iv) will be enhanced to interactive simulations which allow serious gaming either (iv.a) to test the model or to enable the users (iv.b) to learn the space of possible states. The test case will (v) generate some data which can be used to evaluate the model with regard to pre-defined goals. Depending from these findings (vi) one can try to improve the model further.
EXAMPLE
The mayor of a city has the identified problem P that there exists a certain road which has a to high load of traffic. He wants to find a new configuration S which minimizes this problem without creating a new problem P’.
He decides to attack this problem not by delegating it to a group of experts only but to a group of experts collaborating with all the citizens which think to be affected by this problem and a possible solution. Thus the mayor opts for a bottom-up approach. This poses the challenge to find a procedure which enables the inclusion of the citizens in the overall process.
An overview to the enhanced AAI theory version 2 you can find here. In this post we talk about the second chapter where you have to define the context of the problem, which should be analyzed.
DEFINING THE CONTEXT OF PROBLEM P
A defined problem P identifies at least one property associated with a configuration which has a lower level x than a value y inferred by an accepted standard E.
The property P is always part of some environment ENV which interacts with the problem P.
To approach an improved configuration S measured by some standard E starting with a problem P one needs a process characterized by a set of necessary states Q which are connected by necessary changes X.
Such a process can be described by an actor story AS.
All properties which belong to the whole actor story and therefore have to be satisfied by every state q of the actor story are called non-functional process requirements (NFPRs). If required properties are are associate with only one state but for the whole state, then these requirements are called non-functional state requirements (NFSRs).
An actor story can include many different sequences, where every sequence is called a path PTH. A finite set of paths can represent a task T which has to be fulfilled. Within the environment of the defined problem P it mus be possible to identify at least one task T to be realized from some start state to some goal state. The realization of a task T is assumed to be ‘driven’ by input-output-systems which are called actors A.
Additionally it mus be possible to identify at least one executing actor A_exec doing a task and at least one actor assisting A_ass the executing actor to fulfill the task.
A state q represents all needed actors as part of the associated environment ENV. Therefore a state q can be analyzed as a network of elements interacting with each other. But this is only one possible structure for an analysis besides others.
For the analysis of a possible solution one can distinguish at least two overall strategies:
Top-down: There exists a group of experts EXPs which will analyze a possible solution, will test these, and then will propose these as a solution for others.
Bottom-up: There exists a group of experts EXPs too but additionally there exists a group of customers CTMs which will be guided by the experts to use their own experience to find a possible solution.
EXAMPLE
The mayor of a city has identified as a problem the relationship between the actual population number POP, the amount of actual available living space LSP0, and the amount of recommended living space LSPr by some standard E. The population of his city is steadily interacting with populations in the environment: citizens are moving into the environment MIGR- and citizens from the environment are arriving MIGR+. The population, the city as well as the environment can be characterized by a set of parameters <P1, …, Pn> called a configuration which represents a certain state q at a certain point of time t. To convert the actual configuration called a start state q0 to a new configuration S called a goal state q+ with better values requires the application of a defined set of changes Xs which change the start state q0 stepwise into a sequence of states qi which finally will end up in the desired goal state q+. A description of all these states necessary for the conversion of the start state q0 into the goal state q+ is called here an actor story AS. Because a democratic elected mayor of the city wants to be ‘liked’ by his citizens he will require that this conversion process should end up in a goal state which is ‘not harmful’ for his citizens, which should support a ‘secure’ and ‘safety’ environment, ‘good transportation’ and things like that. This illustrates non-functional state requirements (NFSRs). Because the mayor wants also not to much trouble during the conversion process he will also require some limits for the whole conversion process, this is for the whole actor story. This illustrates non-functional process requirements (NFPRs). To realize the intended conversion process the mayor needs several executing actors which are doing the job and several other assistive actors helping the executing actors. To be able to use the available time and resources ‘effectively’ the executing actors need defined tasks which have to be realized to come from one state to the next. Often there are more than one sequences of states possible either alternatively or in parallel. A certain state at a certain point of time t can be viewed as a network where all participating actors are in many ways connected with each other, interacting in several ways and thereby influencing each other. This realizes different kinds of communications with different kinds of contents and allows the exchange of material and can imply the change of the environment. Until today the mayors of cities use as their preferred strategy to realize conversion processes selected small teams of experts doing their job in a top-down manner leaving the citizens more or less untouched, at least without a serious participation in the whole process. From now on it is possible and desirable to twist the strategy from top-down to bottom up. This implies that the selected experts enable a broad communication with potentially all citizens which are touched by a conversion and including the knowledge, experience, skills, visions etc. of these citizens by applying new methods possible in the new digital age.
An overview to the enhanced AAI theory version 2 you can find here. In this post we talk about the first chapter where you have to define the problem, which should be analyzed.
DEFINING THE PROBLEM
Generally it is assumed that the AAI theory is embedded in a general systems engineering approach starting with the clarification of a problem.
Two cases will be distinguished:
A stakeholder is associated with a certain domain of affairs with some prominent aspect/ parameter P and the stakeholder wants to clarify whether P poses some ‘problem’ in this domain. This presupposes some explained ‘expectations’ E how it should be and some ‘findings’ x pointing to the fact that P is ‘sufficiently different’ from some y>x. If the stakeholder judges that this difference is ‘important’, than P matching x will be classified as a problem, which will be documented in a ‘problem document D_p’. One can interpret this analysis as a ‘measurement M’ written as M(P,E) = x and x<y.
Given a problem document D_p a stakeholder organizes an analysis to find a ‘solution’ which transfers the old ‘problem P’ into a ‘configuration S’ which at least should ‘minimize the problem P’. Thus there must exist some ‘measurements’ of the given problem P with regard to certain ‘expectations E’ functioning as a ‘norm’ as M(P,E)=x and some measurements of the new configuration S with regard to the same expectations E as M(S,E)=y and a metric which allows the judgment y > x.
From this follows that already in the beginning of the analysis of a possible solution one has to refer to some measurement process M with an accepted standard E, otherwise there exists no problem P and no possible solution.
EXAMPLE
The mayor of a city wants to know whether the finances of his city x are in a good state compared to some well accepted standards E. Already the definition of a ‘good state’ of the finances can pose a problem. Let us assume that such a standard E exists and the standard tells the mayor that a ‘good state’ for his finances would ideally equal y or all values ‘above y’. If the measurement M(x, E) would generate a result like x < y, then this would indicate in the ‘light of the standard E’ that his city has a problem P. Knowing this the mayor perhaps is interested to analyze this problem P by organizing a process which gives him as a result a configuration S which generates after a measurement M(S,E) the further result that x = y or even x > y. Thus this new configuration S would be an attractive state which should be a valuable goal state for his city.
Change: 6.February 2019 (Reformulating the ‘CONTEXT’ paragraph)
Change: 27.February 2019 (changing the order of the table of contents)
Change: 20.April 2019 (New section ‘The Big Picture…’)
Change: 3.-4.May 2019 (New section ‘Engineering and Society…’ and ‘Simulation and Gaming…)
Change: 5.May 2019 (Bringing the ‘bottom-up’ case in the background; it is now included in the normal AAI analysis)
CONTEXT
In a previous post I started the re-formulation of the general framework of the AAI theory. I decided to organize the text now in a more flexible way: One main post for the overview of all topics and then for every topic an individual post with possibly more detailed extensions. This will generate a tree-like structure with the root-post at level 0 and from this following the links you will reach the posts of level 1, then level 2 and so forth. The posts from level 0 and level 1 will be highly informal; the posts from level 2 and higher will increasingly become more specialized and associated with references to scientific literature. This block is inspired by many hundreds of scientific papers and books.
