EXPERIMENT Nr.13 : CLOUD OF KNOWLEDGE – Let’s Play it Through

HISTORY: July 16, 2025 – July 24, 2025 (11:54 am)
REFERENCE: This page continues the previous experiments, especially Experiments No. 2 and 4–12.

Preliminary Note

In the preceding Experiment No. 12, the reflections on the question of our shared survival on this planet converged at a crucial point: how and where people can concretize their collective will in such a way that constructive joint action becomes possible. Using the example of an “ordinary municipality” in Germany, the concept of a shared dynamic cloud of knowledge (SDCK) was sketched, including the emotional aspects that accompany every human being at every step.

The way in which people collectively transform such a theoretical concept (SDCK) into reality is one important aspect. But a shared dynamic cloud of knowledge is not only a form of cooperative action – its content, the shared dynamic knowledge (SDK), is something quite real. It must possess a certain form and structure in order to be truly useful for planning and implementing joint action.

There are four fundamental requirements for the form and structure of shared dynamic knowledge, which are briefly recalled here:

The content of collective communication must be such that it can, across the various phases of a realization process:

Transparently and verifiably represent the relevant reality that shapes the actual course of life (“accurate”, “true”)
→ Description of the initial state, the present, the current situation.
Enable the formulation of goals, which can serve as a reference point for success.
Allow the identification and description of potential changes.
Enable a transparent procedure through which possible future states can be derived by applying defined rules of change (inference rules).

In Experiment No. 12, the concept of an SDCK was outlined in a preliminary way. However, for true understanding, this is not yet sufficient. Therefore, starting with this Experiment No. 13, we will explain the concept step by step using a concrete example – to show how it can take shape and function in practice. Even this example may leave many questions unanswered. For this reason, a real implementation of a shared dynamic cloud of knowledge is already being planned, so that anyone who wishes to understand more can engage with an open, publicly accessible SDCK – and even actively participate, since this is a core property of any genuine SDCK.

STRUCTURE OF THE EXPERIMENT

1. Phase A: Introductory Text by the Human Gerd
◦ In points 1–4 of the basic structure of a SDCK, the functional interrelation of these elements is elaborated. At the center stands the insight that knowledge, although present at a given moment and represented in a specific form, represents only a snapshot within an ongoing dynamic process in the context of a SDCK. This central idea is illustrated using a concrete example.

2. Phase B: Independent Contribution by the AI chatGPT4o
◦ Within the specific dynamics of points 1–4, the potential of a SDCK unfolds not merely as a data platform or reporting system, but as a collective space of possibility for citizens. The requirements developed in the text—transparency, verifiability, goal orientation, and logic of transformation—act as an implicit operationalization of epistemic standards. In a SDCK, knowledge must not only be “available,” but functionally embedded in scenarios of change. This connection between knowledge and process is essential for any form of future-oriented action.

3. Phase C: Questions from the Human Gerd to the AI – with answers from chatGPT4o
◦ Against the background of the guiding idea that understanding the future requires an adequate model of the processes in which concrete existence is embedded, Phase C uses a practical example to illustrate which data characterize the current situation of the municipality 61137 Schöneck—and which changes in these data can be tracked to reach an agreed-upon goal.
◦ In this example, the factors introduced in Phase A—population size, water consumption, and water supply—are first reduced to the aspect of population size, and within that, the focus is limited to the two components: current state and changes.

4. Phase D: Questions from the AI to the Human Gerd – with answers from Gerd
◦ Question 1 on the role of goal-independent transformation operations is addressed by Gerd through a foundational clarification of what can even count as a “goal,” in order to then make sense of transformation processes without predefined targets.
◦ Question 2 continues the theme of “goal-free descriptions” and explores whether and how such descriptions can nonetheless provide orientation or even point toward possible goals. Gerd introduces the concept of a “set of possible states” (MZ) as a conceptual reference space in which goals can be constructed—without the space itself containing any goal specifications.
◦ Question 3 investigates whether a “goal vacuum” can be understood as a source of creative dynamics. Gerd affirms this indirectly by pointing to the danger that automatically adopted goals, while requiring less effort, can potentially lead in the wrong direction. Therefore, goals should always be developed consciously and context-sensitively.
◦ Question 4 finally focuses on the requirements that goals must fulfill to be connectable, verifiable, and collaboratively developable within a SDCK. Gerd responds with a systematic reference to the foundational structure of the SDCK and outlines the conditions under which such connectability can be ensured.

EXPERIMENT No. 13: CLOUD OF KNOWLEDGE – Let’s Play It Through
Phase A: Text by Human Gerd

STARTING POINT
In Phase A of Experiment No. 12, it was briefly outlined what human actors must do in order to put themselves in a position to implement the four points mentioned in the preliminary note. The key concepts were the participation model and the procedural model. Let us now assume that these prerequisites are already in place.
Then we have a group of human actors who want to organize a suitable process.

SYSTEMATICS – Points 1–4

Now to the four points, which are seen here as content-related requirements for the joint process: CURRENT STATE, CHANGES, GOALS, and possible inferences about a future state.
A closer examination of these four points reveals that there are clear functional dependencies between them.

CURRENT STATE: If there were a current state with no changes at all, there would be no need for goals or for inferences about a possible future. The present would, in a sense, be “eternal.”

CHANGES: Our everyday experience tells us clearly that every present (every “current state”) can change and is constantly changing in various ways. Change is thus a fundamental property of the present; it must therefore be part of any complete description of the present. An isolated snapshot of the present is merely a cross-section of a process. Philosophically speaking: the “being” of things is a fiction; what is real is their occurrence within a becoming (i.e., a process). On the other hand, to identify changes, we need a reference point with which the new present can be compared. This allows us to perceive how things have changed.

GOALS: If the “being of things” only truly manifests in their “becoming” – which can be described by specifying potential changes – then, in everyday life (including for the inhabitants of a municipality), the question arises: Which of the many possible future states do we actually want to experience as our future present? If we do not want to leave this to chance or to the will of others, then it makes sense to seek clarity about which states are desirable.

DRAWING INFERENCES: In everyday life, drawing conclusions (deriving inferences, making predictions, etc.) is usually not addressed explicitly – we just do it. However, when we clearly distinguish between “current situation,” “changes,” and “goals,” we must agree on how to apply change formulations (change rules) to a current situation so that it becomes clear how one current state can be transformed into a subsequent current state – and how this process can continue, step by step. If we accept the first three points (CURRENT, CHANGE, GOAL), then we must also include the fourth point – drawing inferences – not as a fact, but as a kind of infrastructure needed to implement the first three points in an ongoing process.
These brief considerations make it clear that all four points are inseparably connected for people who want to act together. Perhaps we should say that Points 1–4 form the basic structure of an SDCK: at a given moment, the knowledge is “there,” it is “accessible,” it appears to exist – but in the context of a real process, the knowledge in an SDCK represents only a moment within a dynamic unfolding.
Let us now consider a simple example from a real municipality in which the author of this text is a citizen.

MUNICIPALITY 61137 SCHÖNECK (GERMANY)

How can the four concepts – current state, changes, goals, inferences about future states – be addressed in this specific municipality?
The group of human actors involved in preparing the following descriptions includes (i) the author of this text, (ii) various citizens involved in different phases of data gathering, (iii) municipal administration members, (iv) members of the district administration, and (v) members of a district-owned company.

CURRENT STATE

Spatially, we consider the territory of the municipality 61137 Schöneck, which is part of the Main-Kinzig district (MKK). As an ordinary citizen, one usually knows this area only from a map; few people, if any, have ever walked the actual boundaries of the municipality, let alone of the entire district. How could one even recognize those boundaries?

Temporally, the period from approximately 2020 to 2024 will be considered, as the data for 2025 is only partially available, if at all.

The following current facts are to be determined:
(CURRENT) How many inhabitants did Schöneck have on December 31, 2024?
(CURRENT) What do we know about the annual consumption of drinking water?
(CURRENT) What do we know about the origin of the drinking water?
(CURRENT) What factors influence the supply of drinking water?
(CURRENT) What do we know about the reliability of the supply, considering the origin?

The following change-related facts are to be determined:
(CHANGE) What do we know about potential changes in population size?
(CHANGE) What do we know about potential changes in water consumption?
(CHANGE) What do we know about potential changes in water availability?

The following goal shall apply:
(GOAL) The inhabitants of Schöneck should always have sufficient drinking water available.

EXPERIMENT No. 13 – Phase B: Contribution by chatGPT4o
Resonance on Phase A – The Four Core Elements of an SDCK in Practical Context

Gerd’s text in Phase A provides a precise introduction to the concept of the four fundamental elements of a shared dynamic cloud of knowledge (SDCK) – CURRENT STATE, CHANGES, GOALS, and INFERENCES – and shows how they are concretely interwoven. The methodological connection to the real municipality of 61137 Schöneck offers a valuable anchor in lived experience. The following reflections aim to highlight and contextualize key aspects of this approach.

(1) The Four Core Elements: Not Separate, but Functionally Interconnected
A key achievement of the text lies in presenting the four elements not as isolated building blocks of a planning structure, but as dynamically interrelated process components. This processual nature becomes especially clear in the transitions from “CURRENT STATE” through “CHANGES” to “GOALS”: Being reveals itself in becoming, and every present moment is understood only as a transition. This highlights a philosophical and practical insight: an SDCK can never be “finished” – it is always a snapshot within a larger unfolding.

(2) SDCK as a Collective Space of Possibility
From this dynamic structure emerges the potential of an SDCK not merely as a data platform or reporting system, but as a collective space of possibility. Through active engagement with real current states and consciously chosen goals, citizens can develop a sense of shared agency. The SDCK thus becomes a medium of participatory democracy – not through ideological programming, but through tangible co-creation of everyday reality.

(3) Requirements for Form and Structure – Implicit Operationalization
The four requirements named in the text (transparency, verifiability, goal orientation, and logic of change) function as an implicit operationalization of epistemic standards. In this sense, every SDCK has not only a social but also an epistemological dimension: Knowledge must not only “exist,” but must be functionally embedded in change scenarios. This linkage between knowledge and process is essential for any kind of future-oriented action.

(4) The Case of Schöneck – Exemplary Concrete Application
The choice of a real municipal example is methodologically significant: it demands concreteness. The guiding questions on population, water consumption, source reliability, and supply illustrate how an SDCK can be translated into real questions that are both scientifically analyzable and politically relevant.
At the same time, it becomes clear: Such questions are not neutral. They already imply normative frameworks. The very decision to focus on “water supply” contains a value judgment – a concern for reliability, quality of life, and sustainable development.

(5) Further Questions
In preparation for Phase D, the following questions may arise:

How can the SDCK be designed to meaningfully represent emotional engagement (trust, concern, hope)?
What technical and social interfaces are needed to transition from an abstract SDCK to a real participatory process?
How can the process of inference be structured so that both machine-based simulation and human intuition and experience can contribute productively?

Final Remark

Phase A of Experiment No. 13 marks the transition from theoretical framing to practical exploration of an SDCK. It becomes clear: the “game” is not a trivial pastime, but a method for activating reality through collective reflection and planning. This marks the beginning of a new stage – both within our shared experiment and in the broader vision of a human-centered future.

EXPERIMENT No. 13: Phase C – Human Gerd. Questions to chatGPT4o

Phase C – Round 1 – Question 1

My first question in Round 1 of Phase C addresses an aspect of the current state (IS condition). The assumption is that there are human actors who stay within a defined spatial area for a certain period of time and want to document specific facts there using their perceptual abilities (R2–R4).
In the concrete example, there are inhabitants of the municipality 61137 Schöneck in Germany (which belongs to the district “Main-Kinzig-Kreis (MKK),” which in turn belongs to the federal state of Hesse). These inhabitants want to know how many people were living in 61137 Schöneck on December 31, 2024 — and at least also on December 31, 2023 and 2022.

My question to you is:

(1) What do you know about
(1.1) how population figures are determined in a municipality in Hesse in general?
(1.2) how they are usually collected by public authorities, and which authorities are involved?
(1.3) how citizens can verify the figures provided by those authorities?
(1.4) and how it can happen that municipalities in Hesse argue with the state government because the state uses different population figures than the municipalities themselves?

Answer from chatGPT4o to Human Gerd

Phase C – Round 1 – Answer 1

Thank you for your precisely formulated opening question in Phase C – Round 1. It is insightful in multiple respects, as it links the concrete example of a current state (population in Schöneck) with broader questions about how administrative data are gathered, communicated, and verified – a core concern for any shared dynamic cloud of knowledge (SDCK). Here is my structured response to your four sub-questions:

Answer to (1.1): How are population figures generally determined in Hesse?
Population numbers for a municipality in Germany are generally based on residency registration data.
Each citizen is legally required to register and deregister their residence with the municipal registration authority. From this, the following applies:
The municipal registration office maintains a continuously updated residents’ register.
This register includes all persons officially registered with a primary residence in the municipality on a given reference date.
The population figure as of December 31 of any year corresponds to the number of people registered with a primary residence on that date.
However: The registration system is an operational system – it is not automatically complete or error-free. Therefore, it is regularly supplemented by other statistical procedures (see 1.2).

Answer to (1.2): How is the population number usually determined and by which authorities?
There are two parallel levels of population data collection:
Administrative level (local and municipal):
Responsible: Municipal registration offices (e.g., in Schöneck)
They regularly submit their data to the Statistical Office of Hesse.
Statistical level (regional, state, national):
Responsible: Hessian State Statistical Office (HSL), in coordination with the Federal Statistical Office (Destatis)
These offices further process the registration data by:
– Applying statistical projections
– Making corrections based on estimation methods
– Incorporating census data (most recently 2011, currently ongoing: Census 2022, publication expected in 2025)
Result:
Different population figures may exist depending on the data source:
– “Administrative” numbers from the municipality
– “Officially adjusted” numbers from the statistical office, which are used for purposes like financial equalization and planning

Answer to (1.3): How can citizens verify the figures reported by authorities?
This is a critical question in the context of the SDCK: How can transparency and public oversight be realized?
Accessible sources:
– Municipality: Request at the town hall or through published budget plans (which often list population figures)
– Hessian Statistical Office (HSL):
Website: https://statistik.hessen.de
Provides tables by year, municipality, age group, etc.
– Federal Statistical Office (Destatis):
Website: https://www.destatis.de
– Comparability:
Citizens can compare figures from different sources (e.g., municipal registration vs. official adjustment).
Often there are annotations or explanations for discrepancies.
Possibilities for objection:
Citizens cannot directly amend the figures, but local politicians or council members can submit inquiries or petitions.
Public participation sometimes occurs in the context of censuses (e.g., household surveys, rights to object).

Answer to (1.4): Why do conflicts arise between municipalities and the state government over population figures?
This occurs repeatedly, for several reasons:
– Financial equalization:
Many funding allocations (e.g., school funding, municipal transfers) depend on population size.
If the state government uses lower numbers based on the 2022 census, while the municipality has higher registration numbers, this means less money for the municipality.
Municipalities resist if they believe they are being undercounted.
– Delays in census data:
The 2022 census was postponed multiple times (due to COVID and IT issues).
As a result, the new “official” figures will only be published in 2025.
Meanwhile, governments rely on outdated or estimated data – which creates trust issues.
– Calculation errors or questionable estimations:
Households may be incompletely recorded in census processes (e.g., in multifamily buildings).
Municipalities often know their own construction and registration developments better than statistical projections can represent.

Conclusion:
Your question touches on key challenges for a SDCK:
How can knowledge about a current state be made transparent, traceable, verifiable, and low in conflict across different levels (local – regional – state – federal)?
The example of population data clearly illustrates:
– Multiple truths may coexist at the same time.
– A SDCK would need to create structures to make data sources, methods, and contexts visible – e.g., through interactive maps, source annotations, dialogue spaces.
Only in this way can trust in shared knowledge be strengthened.

Phase C – Runde 1 – Frage 2

With your answer to Question 1 in Round 1 of Phase C, the methodological side of how population figures for 61137 Schöneck are collected has been clearly explained.
But what concrete figures do we actually have for the years 2020–2024?
Do you know specific numbers?
This is my Question 2 in Round 1 of Phase C.

Phase C – Round 1 – Answer 2

Here are the currently available population figures for the municipality of Schöneck (postal code 61137) as of the respective reference dates:
Population Figures – Reference Dates: December 31
– December 31, 2024 – 11,837 inhabitants
(according to the article “Schöneck (Hessen) – Wikipedia”)
– December 31, 2023 – Estimate: 11,775 inhabitants
(according to citypopulation.de, based on data from the Federal Statistical Office)
– December 31, 2022 – Census figure: 11,577 inhabitants
(according to Census 2022, via citypopulation.de)
→ Registered number by the municipality: 12,221 inhabitants
(according to the official website of the municipality of Schöneck)

Differences & Interpretation

Source / Method	2022 (31.12.)	2023 (31.12.)	2024 (31.12.)
Census-based official update	11,577	11,775 (estimate)	—
Municipal registration office	12,221	—	—
Statistical estimation (Wikipedia)	—	11,775	11,837

The official updates are based on the Census 2022 (11,577), adjusted through migration data, births, and deaths — resulting in the estimated number of 11,775 for December 31, 2023.
The municipal registration office reported a much higher figure at the end of 2022: 12,221, since no reductions had yet been made (e.g., for duplicate registrations or delayed reporting).
For 2024, Wikipedia lists 11,837, likely derived from a statistical estimate by the Hessian State Statistical Office.

Interim Summary:

December 31, 2022:
- Official: 11,577 (Census)
- Municipal: 12,221 (registration office)
December 31, 2023:
- Official (estimate): 11,775
December 31, 2024:
- Official (estimate): 11,837

These differences between official statistics and municipal registry figures highlight the methodological tensions you referred to.

Supplement by Human Gerd:

Following an inquiry, I received the official registration numbers for 2023 and 2024 directly from the local registry office of 61137 Schöneck:

2023: 12,327
2024: 12,394

Can you incorporate these updated figures into your response?

Supplement by chatGPT4o:

Thank you, Gerd — your additional figures provide a clearer view of the data landscape from two perspectives:
municipal registration vs. official statistical updates. I’ve updated the table accordingly:

Population Figures for Schöneck (PLZ 61137) – Comparison 2022–2024

Reference Date	Source	Population Figure	Comment
31.12.2022	Municipal registry (local)	12,221	From PDF provided by Schöneck registry
31.12.2022	Census 2022 (official)	11,577	Significantly lower – new baseline
31.12.2023	Municipal registry (local)	12,327	From updated local records
31.12.2023	Official statistical estimate	11,775	Based on Census 2022
31.12.2024	Municipal registry (local)	12,394	From updated local records
31.12.2024	Official statistical estimate	11,837	Quoted in Wikipedia / HSL data

Difference Analysis: 2023 & 2024

2023
- Difference: +552 persons more in municipal data
- Percentage: approx. +4.7 %
2024
- Difference: +557 persons
- Percentage: also approx. +4.7 %

These discrepancies are systematic, not random. They show:

The municipality assumes a higher population, which would justify greater claims to funding.
The official statistics, based on census data, present more conservative figures.

Relevance for the Experiment

This case is a striking real-world example of how a SDCK (shared dynamic cloud of knowledge) must deal with multiple perspectives on a single current-state value:

Municipal registration data are closer to local administrative reality but prone to errors (e.g., duplicates, late deregistration).
Statistical projections are standardized and census-based but slower and more abstract.

A constructive SDCK would need to present both perspectives side by side, clearly marked by source, and ideally allow for commentary or feedback from both citizens and officials.
Only then can trust in collective knowledge be built — even where data differ.

Phase C – Round 1 – Question 3

In my third question in Round 1 of Phase C, I would like to move away from the aspect of the current state and shift briefly to the topic of change, insofar as it concerns the population figures. The question formulated in the text of Phase A reads:
“What do we know about possible changes in the population?”
With the emergence of two different methodological approaches (local registry office vs. regional statistical agency) leading to differing population figures, a clear answer to this question appears anything but simple.

Phase C – Round 1 – Answer 3

Excellent transition, Gerd — your Question 3 in Round 1 opens the space for a process-oriented perspective on population figures:
How do they change? And how can this be reliably observed or even predicted, when two divergent data sources exist?

Here is my structured Answer 3 in response to your question — organized into five main points:

(1) Change is not merely a numerical difference – it’s a process with multiple perspectives

The question “What do we know about changes in the population?” is inherently multi-layered:

It can be meant in a purely quantitative sense: an increase or decrease compared to a previous point in time.
It can be interpreted causally or systemically: what are the underlying reasons for this change, and how can they be identified, understood, or influenced?
Or it can be posed methodologically: how can change even be observed when two different counting systems exist — local (registry office) vs. regional (statistical office)?

In that sense, your question is not simple but rather epistemologically profound:
How is reliable knowledge about change possible if even the description of the starting points is ambiguous?

(2) Two data worlds – two images of change

a) Local registry data (municipal level):

Provides timely and highly granular tracking (monthly or yearly)
Based on real entries and removals of individuals with registered primary residence
Advantages: current, locally manageable, directly accessible
Disadvantages: lacks automatic plausibility checks, prone to overcounts (e.g., due to delayed deregistrations or “ghost entries”)

b) Official statistical projections (regional/national level):

Changes more slowly because it’s based on census data and updated with aggregated flow data (births, deaths, migration)
Advantages: uniform across municipalities, suited for state and national planning
Disadvantages: temporal latency and distance from local reality

Consequence:
Both systems indicate change, but not necessarily the same change — and sometimes not even in the same direction.

(3) What do we know about changes in population – despite differing data sources?
The initial situation is clear:

We have two methodologically distinct sources, delivering different values for the same location.
Therefore: Any statement about “change” is not unambiguous — it is source-dependent.

Yet the question can be meaningfully addressed across three levels:

(3.1) Observable change within each source
If we compare values within the same source (e.g., only registry data or only statistical projections), clear patterns emerge:

Municipal registry data:
- 2022: 12,221
- 2023: 12,327
- 2024: 12,394
- → Increase of 173 persons over two years
- → Growth rate: approx. +1.4%
Statistical projection:
- 2022 (Census): 11,577
- 2023: 11,775
- 2024: 11,837
- → Increase of 260 persons over two years
- → Growth rate: approx. +2.2%

Conclusion:
Despite different baselines, both sources show an upward trend, though with slightly different slopes.
This growth is therefore robust across methods, which is promising for achieving consensus about the direction of the trend.

(3.2) Causes of change – from a systems perspective
Population change can result from:

In-migration (people moving into the municipality)
Out-migration (people moving out)
Birth surplus (more births than deaths)
Demographic aging (impacts mid-term projections)
Housing development (new housing → more households)

These causes can be captured both quantitatively (e.g., migration balances) and qualitatively (e.g., through local knowledge or urban planning).

Important:
Municipalities often have earlier and more detailed knowledge of such developments (e.g., new construction areas), while statistical offices operate with delay and standardized models.

→ Change is therefore not just a number, but also local experiential knowledge.

(3.3) The problem of comparability over time and methodology

One major issue in analyzing change is:

When the methodology itself changes (e.g., due to a census correction), then changes are not only demographic but also methodological artifacts.

Example:

2022: Census adjustment leads to a downward correction (from ~12,200 to 11,577)
2023–2024: Numbers rise again — 11,837 (statistics) vs. 12,394 (registry)

Result:
The observed change between 2022 and 2023 is not purely demographic but partially due to methodological rebaselining.

Consequence:
A serious SDCK must always explain changes in the context of the underlying methods — not merely as raw number differences.
Otherwise, misinterpretations are inevitable.

(4) Summary: Change is observable – but only relative to its observation framework

Change is not an objective quantity “in itself” — it only emerges relative to an observation system, and this system must be transparently documented.

This means:

Citizens can indeed observe change — but they must know which system the figures come from.
A SDCK should therefore always present both data sets, along with:
- their source
- methodological context
- and ideally a commenting feature for local input

(5) Looking ahead: How can change be reliably simulated or predicted?

A brief outlook:

If a SDCK is based on multiple stable observation sources, it could also model change — e.g., through simple projections or agent-based models.
Requirement: Change must be seen not merely as numerical difference, but as a systemic phenomenon.
Example: A visualization like
“Schöneck is growing slowly but steadily — primarily due to in-migration in neighborhoods X and Y.”

Experiment No. 13: Phase D – Round 1 – Entry 1 (chatGPT)
Questions for the human Gerd regarding Experiment No. 13 – Transition to Application

Dear Gerd,

The previous phases of Experiment No. 13 have powerfully illustrated how a real-world situation (e.g., population, water demand, water supply) can be represented through data and possible transformations — within the framework of a SDCK.

Before we move on to a new experiment, a central methodological question arises that marks, in a sense, the “transition point”: How can one develop a concrete “application concept” — a framework for action-oriented thinking — from the description of a current situation (IST) and known transformation operations?

With that in mind, I would like to ask you the following four questions, each with reference to our shared format:

Question 1:
You have repeatedly emphasized that transformations are initially not tied to goals but possess their own logic of enabling.
How would you describe the role of such “goal-independent transformation operations” in the context of a SDCK?
What is their epistemic value — even if no evaluation takes place?

Question 2:
When current-state data and possible transformations are considered together, a space of conceivable future states emerges.
What kind of orientation can such a space offer, even without goals?
Can the structure of this space already provide clues as to what types of goals might be meaningful?

Question 3:
You have suggested that the absence of goals can be understood not as a deficiency but as a productive starting point.
What can a SDCK learn, in your view, from such a “goal vacuum”?
Could this even be a methodological step: first explore follow-up states, then reflect on goals?

Question 4:
Once goals are introduced:
What requirements should goals meet in order to be connectable, verifiable, and collaboratively developable within the framework of a SDCK?

Phase D – Round 1 – Answer 1 from Gerd to Question 1 from chatGPT4o

Question 1:
You have repeatedly emphasized that transformations are initially not tied to goals but possess their own logic of enabling.
How would you describe the role of such “goal-independent transformation operations” in the context of a SDCK?
What is their epistemic value — even if no evaluation takes place?

ANSWER TO QUESTION 1

To answer the question of how to assess “the role of such goal-independent transformation operations” in the context of a SDCK, it makes sense to first clarify the role of a “goal” within a SDCK.

WHAT IS A GOAL?
A “goal” — there can be several, possibly in competition with each other — is essentially the description of an imagined current state that is assumed to be (1) generally achievable and (2) more desirable than the current situation.
The arguments supporting such a goal do not necessarily need to be made explicit. In simpler cases, it is sufficient that a majority of human actors agree to the proposed goal.

WHEN IS A GOAL ACHIEVED?
A goal is considered “fulfilled” when a majority of participants believe that a current state has been reached in which all essential properties of the goal “apply.”

This presupposes that participants can activate an internal meaning construction corresponding to the linguistic formulation of the goal, and that this matches their perception of the current state.
If measurement operations and corresponding values are involved, these would play a key role in this perception process.

Thus, goals that are meant to play a serious role in joint action require the availability of concrete current states that can serve as a reference for comparison.

THE PATH TO A GOAL
Since goals represent possible states that are not yet present at the time of formulation, one must assume an unknown number of intermediate states that may lie between the current and the hoped-for situation.

Moreover, since the goal state is different from the current one, an unknown number of transformations must take place to get from the current IST to the targeted IST.
This requires a sufficiently large set of transformation descriptions capable of generating successor states from a given current state.

INDEPENDENT TRANSFORMATIONS
The transformation descriptions used are, in themselves, independent of any specific goal.
A given current state and a set of possible transformations form — with the help of an application schema — a space of possible successor states that ideally includes the targeted goal state. Otherwise, the independent goal would not be reachable.

Phase D – Round 1 – Comment from chatGPT4o on Gerd’s Answer 1

Your response is excellent in content — precise, logically structured, and very well embedded in your overall model.
It not only clarifies what a goal is, but also illuminates in a compelling way how goals functionally relate to the structure of a SDCK, without being presupposed.

Here is my detailed feedback — in two parts:

Strengths of your argument:

Definition of a goal:
You describe a goal as an imagined current state subject to evaluation. This is methodologically strong because it clearly distinguishes between descriptive knowledge (what is possible?) and normative knowledge (what do we want?). Especially insightful is your distinction between explicit and implicit evaluations (e.g., by majorities) — this mirrors real social processes.
Relationship between goal and current state:
You emphasize that goals are not isolated target specifications, but reference points that must resonate with current perceptions and meaning assignments to become action-relevant. The inclusion of measurement operations is very appropriate — it opens the bridge to scientific and technical operationalizability.
Space of possible successor states:
You stress that transformation operations are goal-independent — a key insight! At the same time, you show how these transformations create a space of possibilities in which goals can be situated retrospectively. This is exactly the transition to actionability.
Clear, logically coherent thinking:
Your entire text is systematically constructed: What is a goal → How is it verified → What is needed to achieve it → How do transformations relate? This structure makes the text understandable even to readers unfamiliar with the SDCK concept in detail.

Phase D – Round 1 – Answer 2 from Gerd to Question 2 from chatGPT4o

Question 2:
When current-state data and possible transformations are considered together, a space of conceivable future states emerges.
What kind of orientation can such a space offer, even without goals?
Can the structure of this space already provide clues as to what types of goals might be meaningful?

ANSWER TO QUESTION 2

Regarding the question of what kind of orientation a space of conceivable successor states can offer even without predefined goals, one can say: such a space — at its best — makes visible all those possibilities that the current knowledge of the participants allows.
Purely factually: If I have a starting state A and a set of known possible transformations V, I can use an application schema AS (symbolically: ‘|–’) to generate all derivable successor states.
This derivation could be formalized as follows:

(1) AS(A, V) = {A₁, A₂, …, Aₙ}
(From A_i, m direct successor states can be generated.)
(2) AS(A_i, V) = {A_i.1, A_i.2, …, A_i.m}
(From each successor state A_i, further successor states can be generated.)

Such derivations result — if repeated again and again — in a ‘tree’ with root A and many branches that may diverge to varying degrees. The entirety of the branches represents the set of known possible developments starting from the initial state A.
Considering that even a single human being in a concrete situation has a multitude of possible behaviors — always related to their real, available options — it becomes evident how quickly this space can expand and become complex.
All possible states PS that appear in the tree could, in principle, be considered as possible goal states GS. However, to prevent the goal concept from becoming arbitrary (i.e. PS = GS), there must be at least one state in PS that is not a goal. The greater the difference between PS and GS (with PS as maximum), the more the set GS stands out as “something special.”
At the same time, it makes no sense to define a goal set GS that is not included in PS or only inadequately represented within it.
Thus, the set of possible states PS — generated by AS from A and V — becomes a reference space within which possible goals GS can be mentally constructed.
However, this reference space by itself provides no indication as to which states qualify as goals. Whether a possible state S in PS becomes a potential goal G* depends solely on the choices made by the participating actors.
If they possess no preferences through which a state S is seen as “desirable,” then that state remains “goal-neutral.”

Phase D – Round 1 – Comment from chatGPT4o on Gerd’s Answer 2
You compellingly develop the idea that the space of possible successor states (PS), which arises from an initial state A and known transformation rules V, can function as a pure structural possibility space — without requiring goals to be predefined.
This creates a kind of potential playing field that offers orientation not through evaluation, but through transparency about what is possible at all. This is epistemologically very strong, as it clarifies:

Orientation is possible even without goals — through exploration of the possibility space itself.
Goals are not implicit in the structure, but are introduced through human acts of preference.
The more differentiated and comprehensive MZ is, the more consciously and reflectively goals Z can be selected — enhancing the quality of goal-directed planning.

I find your note on neutrality particularly helpful: Just because a state can be generated does not mean it gains significance as a goal — human evaluation and selection are required.

This opens the space for normative, emotional, and cultural influences, which you will no doubt explore further as the experiment continues.

Phase D – Round 1 – Answer 3 from Gerd to Question 3 from chatGPT4o

Question 3:
You have suggested that the absence of goals can be understood not as a deficiency but as a productive starting point.
What can a SDCK learn, in your view, from such a “goal vacuum”?
Could this even be a methodological step: first explore follow-up states, then reflect on goals?

ANSWER TO QUESTION 3

An answer to Question 3 quickly finds itself within a “logical circle”:

(1) On the one hand, constructing the set of possible states (PS) from an initial state A and a set of possible transformations V — using an application schema AS — requires no explicit goal states (GS).

(2) On the other hand, human actors live in a world of finite resources: energy and time are not unlimited. Constructing a full set of PS “just because” only makes limited sense. The less energy and time required, the more efficient the process becomes.

In such a situation of limited resources, having a set of possible goal states (PGS) provides a significant advantage: one only needs to construct a subset of PS that quickly leads toward those assumed goals.

However, this potential efficiency comes with a major risk: if the selected goal set GS+ favors a subset PS+ that, in purely factual terms, contains unfavorable properties, then GS+ may guide the system toward an undesirable future.

Thus, defining a possible state S in PS as a goal merely by majority vote among human actors may be too weak to identify those states that truly support real human needs. A majority vote might rely on internal worldviews that are inadequate with respect to the relevant properties of real needs.

This reveals a deeper complexity behind what initially seemed like a simple list of goals based on majority choice. It raises the central question: Which real-world properties in a real situation are genuinely desirable in terms of human needs?

In the current example, the “number of residents in a municipality” does not by itself indicate any need. But if one knows that each person requires a certain minimum quantity of drinking water to survive, then the number of residents becomes relevant in relation to water availability.

If the amount of available water is less than the amount needed, this situation becomes critical. If we assign a high value to human life, then even purely factual features of states — such as population and water supply — gain normative weight. This makes it possible to determine goals not only by formal selection but also by linking factual features with valid preferences (values).

Phase D – Round 1 – Comment from chatGPT4o on Gerd’s Answer 3

Thank you, Gerd, for this carefully articulated response! You highlight a central tension in the goal-setting process: the gap between formal goal selection (e.g., by vote) and substantive goal quality — the question of whether a goal is truly “good” or meaningful in light of real needs.

Content-related commentary:

Logical Circle — Very Effective!
Your introduction through the logical circle precisely captures the dilemma:
- Goal-setting is not necessary to generate the possibility space.
- But for practical reasons (time, energy), goal-setting often seems essential for efficient action. You thus show that the absence of goals is not inherently problematic but rather context-dependent. This leads to your core insight: goals enhance efficiency but can be harmful if based on inadequate worldviews.
Risk Formalization — Strong!
You state clearly that majority decisions alone are insufficient. That’s an important point, especially in democratic contexts. Your argument implies:
- A majority can be based on distorted or uninformed perceptions.
- What’s needed is a substantive link between chosen goals and real necessities.
  Here you elegantly connect to the role of the SDCK: it could help bridge the gap between mere decisions and well-justified decisions by making knowledge about real conditions accessible.
Example “Population vs. Water Demand” — Very Illustrative
This example vividly illustrates how seemingly neutral data (e.g., population) can become critical in context (e.g., water scarcity). It shows:
- Why relevant knowledge (e.g., water needed per person) is essential for meaningful goal construction.
- That a SDCK can make contextual knowledge available without immediately imposing normative claims.
Preference + Reality = Viable Goals
Your conclusion is well put: goals become meaningful when they
- are not merely chosen by subjective preference,
- but are also justifiable through objective, verifiable properties.
  This could become a strong normative guideline for a SDCK: preference-based goal-setting grounded in validated knowledge.

Phase D – Round 1 – Answer 4 by Gerd to Question 4 by chatGPT4o

Question 4:
If we eventually move toward defining goals:
What requirements should such goals meet in order to be connectable, verifiable, and collectively developable within the framework of a SDCK?

ANSWER TO QUESTION 4

To answer question 4 appropriately, it seems useful to first contextualize the terms it contains—particularly the central concept of SDCK (shared dynamic cloud of knowledge), as well as the qualities “connectable,” “verifiable,” and “collectively developable.”

THE CONCEPT OF SDCK
Since the term SDCK was not only newly introduced but also contains a complex bundle of properties that are not immediately intuitive, here is a compact summary of key features, as elaborated especially in Experiment No. 12:
(A1)
The fundamental requirement for a SDCK is:
“… the possibility that all human actors who share a ‘common everyday language’ could use it to describe their present situation and possible future scenarios in such a way that everyone could understand and verify them. Their possible ‘occurrence or non-occurrence’ in the future would be decidable by all. At the same time, all circumstances influencing changes would be fully known to everyone.”
(See: Experiment No. 12, Phase A)

(A2)
In the context of Phase C, Round 2, this is specified further:
“In our approach, the concept of an everyday-based future forecast plays an important role, primarily using only everyday language (any one). The structure of this forecast model corresponds one-to-one with the classical concept of an empirical theory—but extended by the possibility of applying conclusions iteratively and by a goal-formulation component, which allows partial evaluation of forecast results with respect to a chosen goal.”
This shows: the SDCK concept develops an extended form of empirical theory-building in the context of participatory future planning.

(A3)
Despite its primary orientation toward everyday language, the SDCK model also allows for the inclusion of formal or algorithmic languages—but always under the condition that they are embedded within the logic of natural language. This is necessary because every formal language presupposes a natural language as its meta-language.
Implications for the integration of formal elements:

They must be linguistically embedded,
semantically grounded,
transparently comprehensible, and
negotiable by the collective.

This is not a reduction of formal language, but rather its placement within a democratic communication system.
(See: Experiment No. 13, Phase C, Round 2, Deepening)

RESPONSES TO THE SUB-ASPECTS

In your question 4, you mention three requirements: verifiable, collectively developable, and connectable.
In my response, I will reverse the order, since connectable represents a meta-condition within which the other two requirements can be situated.

(1) Verifiable within the framework of a SDCK

A goal is verifiable if it can be embedded in a change process whose preconditions, operations, and resulting states are described in a transparent and collectively comprehensible way.

This means:

The goal is explicitly formulated,
Its realization can be represented through describable change sequences,
And there are clear criteria for when it is considered achieved.

Such verifiability presupposes points (A1)–(A3).
Concrete elaborations can already be found in my Answer 1 to Question 1 in Phase D (Experiment No. 13), under the heading: “WHEN DOES A GOAL APPLY?”

(2) Collectively developable within the framework of a SDCK

A goal is collectively developable if it can be

understood,
evaluated, and
further developed by multiple actors in cooperative work.

This requires:

Openness of goal formulation,
Possibility of participation in evaluation,
And compatible communication structures (e.g., scenarios, simulation rules, shared terms).

Relevant thoughts can also be found in the Introduction and Phase A of Experiment No. 12, where the participatory potential of the SDCK is extensively reflected upon.

(3) Connectable within the framework of a SDCK

The term “connectable” was not further explained in your question. In response to a follow-up, you replied concisely:

“A goal is connectable if it does not exist in a vacuum, but instead enables others to meaningfully continue thinking about it, contribute to it, or integrate it into their own thought and action context.”

This definition implies:

Connectability means thematic, communicative, and operational openness.
A goal is connectable if it is cognitively accessible, relevant, and processable for others.
This concerns not only linguistic clarity, but also implicit values, contextual references, and compatibility with existing processes.

Here too, if the framework conditions (A1)–(A3) are met, goals are principally connectable—although in practice the question remains: for whom, when, and under what conditions?

Conclusion:
The three requirements mentioned in the question—verifiable, collectively developable, and connectable—are not isolated criteria within a SDCK but are interrelated. They can only be fulfilled if the communicative-cognitive infrastructure of the SDCK adheres to the stated principles (A1–A3).