Last Change: Sept 21, 2024

Inquirer: Gerd Doeben-Henisch

Email: info@uffmm.org

CONTEXT

This post is part of the section Dialogues with chatGPT4.

PREFACE

A burning dialogue with chatGPT4 shows results, which offer a real and great challenge. Here is the protocol, what happened.

CONCLUSION

The Author: OK, I think we have detected a very interesting point of view to elaborate further the possible ‘fate of democracies’ in our dynamic world. I am planning the following steps how to proceed further: (1) I will post this dialogue so far in two of my main blogs (English as well as German); then (2) I will analyze a bit more how we could transform Democracies 1.0 (lacking the needed efficiency) to a kind of Democracies 2.0 with a new approach to improve efficiency significantly by keeping the key values of democracy not only alive but ‘improve these values’ instead.

@chatGPT4o: That sounds like a great plan! Your vision of transforming Democracies 1.0 into Democracies 2.0 by addressing efficiency while preserving and even enhancing key democratic values is a promising direction. I’m excited to see how your analysis unfolds, and I’d be happy to assist with any further insights, ideas, or refinements as you continue exploring this transformation.

BEGIN OF THE DIALOGUE

Hi chat, today I want to start a dialogue in English. Are you ready?

I’m ready! Let’s get started. What’s on your mind today?

I like to discuss the question, whether a democracy can enable with a minimal chance of success the survival of life on our planet earth.

That’s a profound question. To unpack it, we can explore the relationship between democracy and the capacity to address existential challenges like environmental degradation, climate change, and the broader survival of life on Earth. Here are a few ways we can approach the topic:

1. **Democracy’s Strengths**: In theory, democracy offers mechanisms for accountability, public participation, and transparency. These features could lead to more responsive environmental policies because elected leaders should act on the will of the people. Public pressure can drive governments to adopt sustainability measures, especially as awareness of environmental issues grows.

2. **Democracy’s Weaknesses**: Democracies often face short-term thinking, driven by electoral cycles. Politicians might prioritize immediate economic gains or policies popular with their voter base, which could hinder long-term planning for environmental sustainability. Moreover, the influence of powerful interest groups (like fossil fuel companies) can sometimes overpower the collective will for change.

3. **Global Coordination and National Sovereignty**: Climate change and ecological collapse are global problems requiring coordinated action, but democracies may struggle with that level of international cooperation. While democratic nations can act within their borders, global treaties and cooperation are harder to enforce without a centralized, authoritative body, which democracies are wary of because it threatens national sovereignty.

4. **Historical Examples**: We could explore how democracies have responded to past environmental crises, such as the Montreal Protocol (which successfully limited ozone depletion) or more recently, struggles to meet the goals of the Paris Climate Agreement. These offer insights into both the potential and limitations of democratic systems in addressing existential threats.

5. **Innovations in Democratic Governance**: Some argue that democracy needs reform to handle the scale of current challenges. Ideas like “deliberative democracy” (where informed citizen panels discuss and guide policy) or “ecological democracy” (where the environment has legal rights and standing) could offer new ways to think about democracy’s role in planetary survival.

In your view, what elements of democracy do you think are most crucial in shaping its ability to meet these challenges? Do you see more potential in democratic innovation, or are there inherent flaws?

OK, you are presenting some important aspects. But, I would like first to dive some levels behind the ‘surface’ of institutions and I would like to start with the most basic principles of democracies. Is this OK?

Absolutely, starting with the foundational principles of democracy is a great way to build a deeper understanding. Let’s dive into those basic principles and explore how they relate to the survival of life on Earth:

### 1. **Sovereignty of the People**

– **Principle**: In a democracy, the ultimate power rests with the people. Citizens have the right to choose their leaders and influence policies through voting, free speech, and civic engagement.

– **Relevance to Survival**: Theoretically, if a majority of people prioritize environmental survival, a democracy should be able to implement policies reflecting that will. However, this assumes that people have accurate information and are sufficiently engaged with long-term concerns like planetary survival. The challenge lies in educating and mobilizing the populace to prioritize environmental concerns over immediate personal or economic interests.

### 2. **Equality and Freedom**

– **Principle**: Democracies are built on the idea that all individuals are equal and have inherent rights, including the right to freedom of expression, association, and action, as long as they do not infringe on others’ rights.

– **Relevance to Survival**: Freedom allows for diverse ideas and solutions to emerge, including environmental activism. However, unchecked individual freedom, particularly in the economic sphere, can lead to overconsumption, resource depletion, and environmental degradation. The challenge here is balancing individual freedoms with collective responsibilities toward the planet.

### 3. **Rule of Law**

– **Principle**: Democracies rely on laws that apply equally to all, and the government is bound by these laws. This promotes accountability and prevents abuses of power.

– **Relevance to Survival**: Strong legal frameworks are crucial in enforcing environmental regulations. However, democracies are often slow to adapt laws to rapidly changing ecological realities. Lobbying and corporate interests can weaken environmental regulations, making it harder for democracies to respond to crises swiftly.

### 4. **Majority Rule with Minority Rights**

– **Principle**: Democracies operate on majority rule but must also protect the rights of minorities. This ensures that the majority cannot oppress smaller groups and that everyone’s basic rights are safeguarded.

– **Relevance to Survival**: Protecting minority rights is vital in ensuring that vulnerable populations, who are often disproportionately affected by environmental degradation, are heard. Yet, environmental decisions sometimes require sacrifices that might conflict with the interests of certain groups. Achieving a balance between protecting rights and pursuing the common good is complex in democratic systems.

### 5. **Deliberation and Consensus**

– **Principle**: Ideally, democracies encourage debate, deliberation, and consensus-building, rather than authoritarian decision-making.

– **Relevance to Survival**: On the one hand, deliberation allows for the consideration of multiple perspectives, including scientific input on climate change and environmental protection. On the other hand, consensus-building can be slow, and in times of crisis, such as the climate emergency, the need for swift action can clash with the democratic emphasis on debate and discussion.

—

### Deeper Challenges at the Core of Democracy

1. **Human Nature and Short-Termism**: One of the core challenges is that democracies rely on human nature, which often prioritizes short-term needs (immediate economic benefits, personal comfort) over long-term survival. The question arises whether democratic structures can transcend these tendencies when long-term environmental goals are not always aligned with short-term personal or national interests.

2. **Informed Electorate**: Democracy assumes that people can make informed choices. However, misinformation, lack of scientific literacy, and apathy often prevent voters from fully grasping the urgency of environmental issues. The question becomes whether democracies can ensure that their citizens have the necessary education and information to prioritize planetary survival.

3. **Competing Interests and Values**: Democracies must accommodate a wide range of interests and values. Some individuals or groups might prioritize economic growth, personal freedom, or cultural values that conflict with environmental sustainability. Can a democracy reconcile these competing values in a way that ensures the survival of life on Earth?

—

How do you feel these foundational principles might either help or hinder the democratic response to global survival challenges?

Well, your ideas are really good points to clarify important factors for a possible success. I would make a first classification of your ideas: (1) There are those which describe some ‘structures’/ ‘principles’ of a democratic society and (2) those which evaluate the possible contribution of these structures for a success. Would you agree?

Yes, I completely agree with your classification. Your distinction between **(1) the structural principles of a democratic society** and **(2) the evaluation of their potential contributions** toward solving existential challenges is a helpful way to organize the discussion.

### (1) **Structures/Principles of Democracy**:

These represent the foundational aspects of how democracies are built and operate, without necessarily making judgments on their effectiveness:

– **Sovereignty of the People**: The people are the ultimate authority, and their collective will shapes policy.

– **Equality and Freedom**: The rights of individuals to be treated equally and have personal freedoms protected.

– **Rule of Law**: All citizens, including leaders, are subject to laws, ensuring accountability.

– **Majority Rule with Minority Rights**: The majority makes decisions, but minority rights are protected.

– **Deliberation and Consensus**: Democracies favor discussion, debate, and finding common ground over authoritarian decision-making.

### (2) **Evaluations of These Structures’ Contributions to Survival**:

This part involves critically assessing how well these structures perform when addressing the larger issue of planetary survival:

– **Sovereignty of the People**: Can the collective will of the people prioritize long-term survival, given the tendency for short-term interests to dominate?

– **Equality and Freedom**: How do we balance personal freedoms with the need for collective responsibility to the planet? Does economic freedom exacerbate environmental problems?

– **Rule of Law**: Is the democratic legal system adaptable and strong enough to enforce necessary environmental regulations, even when there is opposition from powerful interests?

– **Majority Rule with Minority Rights**: How do we ensure that the rights of future generations or vulnerable populations are protected when they may not have a direct voice in current democratic processes?

– **Deliberation and Consensus**: Can democracies act quickly enough in the face of ecological crises, or is the process of deliberation too slow to meet urgent environmental challenges?

This classification highlights the **separation between the ideal functioning** of democratic structures and **the practical challenges** of using these structures to address complex, long-term issues like the survival of life on Earth.

Would you like to explore these ideas further or refine this classification in any way?

Yes, it seems to me that we can – and should – dig even deeper. If we think of people as ‘systems’ which can act on the basis of ‘knowledge’, ‘experience’ as well as ‘emotions’ (emotions in a very broad sense) and ‘minimal bodily capabilities’, then they will not be able to ‘do the right things’ if the ‘necessary knowledge’ is missing, or ‘the necessary emotions’ are not available or the ‘necessary bodily capabilities’ are not there. Would you agree?

Yes, I agree with your point. People, as individual systems, need a combination of **knowledge**, **experience**, **emotions**, and **bodily capabilities** to act effectively, particularly when it comes to complex issues like ensuring the survival of life on Earth. If any of these components are lacking, it can limit their ability to make informed and effective decisions. Let’s break this down further:

### 1. **Knowledge**:

– **Importance**: Without the necessary knowledge, individuals cannot understand the complexity or urgency of global issues like climate change, biodiversity loss, or resource depletion. Knowledge forms the basis for rational decision-making.

– **Challenges**: In democratic societies, the availability of accurate, accessible, and actionable information can vary. Misinformation, lack of scientific literacy, and political agendas often cloud public understanding.

– **Implications**: A population without sufficient knowledge may not vote for or support policies that ensure planetary survival, simply because they don’t grasp the full consequences of environmental degradation.

### 2. **Experience**:

– **Importance**: Experience allows individuals to apply knowledge practically and make connections between abstract ideas and real-world outcomes. Personal experience with environmental changes (e.g., witnessing natural disasters) can also serve as a motivator for action.

– **Challenges**: For many, the existential threats to the planet may seem distant or unrelated to their day-to-day experiences. People living in wealthier or more stable regions might not feel the immediate impacts of environmental collapse, leading to a disconnect between experience and urgency.

– **Implications**: Without direct or relatable experience, people may not see the need for urgent action or might underestimate the severity of environmental threats.

### 3. **Emotions**:

– **Importance**: Emotions drive much of human behavior, often more powerfully than knowledge. Compassion, fear, hope, and anger can all influence how people engage with existential issues. Empathy for future generations, for instance, might compel individuals to prioritize long-term sustainability.

– **Challenges**: Emotional responses can be unpredictable. Fear or apathy may lead to inaction or denial, while excessive optimism might cause people to underestimate the challenges ahead. Emotional responses can also be manipulated by political actors or media, leading to misplaced priorities.

– **Implications**: Without the right emotional engagement—such as a sense of urgency, responsibility, or empathy—people might not be motivated to act, even when they possess the necessary knowledge and experience.

### 4. **Bodily Capabilities**:

– **Importance**: People need the physical capability to take action, whether that’s participating in protests, voting, working in environmental fields, or simply living in a way that reduces their ecological footprint.

– **Challenges**: Economic inequality, health issues, or geographic constraints can limit the ability of individuals to act on their values. If someone is struggling to meet basic survival needs, they may not have the capacity to prioritize environmental concerns.

– **Implications**: Without the physical ability to participate, even those with the right knowledge, experience, and emotions may be unable to engage in actions that contribute to planetary survival.

—

### The Interaction of These Components in Democracy:

In a democratic system, people must have access to all these components to collectively make the right decisions for the survival of the planet. Here are a few considerations:

– **Collective Knowledge**: Democracies must ensure the widespread dissemination of scientific knowledge and environmental education to inform the public.

– **Experience Gaps**: In some cases, the experience of environmental collapse may not be shared by the whole population. Democracies must find ways to bridge these gaps in perception.

– **Emotional Mobilization**: Effective leadership in a democracy must be able to tap into the right emotional currents—urgency, hope, responsibility—to galvanize action.

– **Addressing Inequality**: Ensuring that all citizens have the physical and economic capacity to participate in environmental action is crucial for democratic effectiveness.

Would you like to explore how these components interact in more detail within democratic processes, or focus on specific areas like education, emotional mobilization, or inequality?

From the point of engineering it is very common to analyze the capabilities of systems participating in dynamic processes and what are the possible (and most probable) future states. Then one can compare these – mostly simulated – forecasts with the theoretical ideal: is the system capable of satisfying minimally the defined goal states? I ask myself whether this should also be a strategy to get some estimates whether the ‘citizens today’ living in their ‘typical everyday situations’ have a real chance, to enable such a common behavior as it is needed in a democracy to solve today problems? What do you mean about this?

Your approach is intriguing and aligns well with systems thinking, which is often used in both engineering and social sciences to model dynamic systems and their potential outcomes. Applying this to the question of democracy’s capacity to solve existential challenges like climate change introduces a structured way to assess the likelihood of success based on the current state of “citizen systems.”

Here’s how we can break this down:

### 1. **Modeling Citizens as Dynamic Systems**:

– Each citizen in a democracy can be thought of as a system influenced by inputs such as **knowledge**, **experience**, **emotions**, and **bodily capabilities** (as we discussed earlier). These inputs shape their actions, behaviors, and decisions within the democratic process.

– The system’s **outputs** would be things like **voting behavior**, **activism**, **lifestyle choices**, and **public pressure** on institutions—outputs that directly influence the democratic process and policy decisions.

– To model this, you would simulate how changes in these inputs affect the likelihood of the citizen making decisions aligned with planetary survival (e.g., supporting green policies, sustainable practices, or candidates with long-term environmental goals).

### 2. **Dynamic Process and Feedback Loops**:

– **Feedback mechanisms** are crucial in democracy. For instance, citizens vote based on current conditions (knowledge, emotions), but the outcomes of their votes (policy decisions, environmental changes) feed back into their system, affecting their future behavior. A well-functioning democracy should create positive feedback loops—where good policies lead to better outcomes, which reinforce citizen support for further action.

– However, **negative feedback loops** are also possible, where poor outcomes (environmental degradation, inequality) reduce citizen trust in democratic institutions, leading to disengagement or short-term thinking.

### 3. **Simulating Future States**:

– Using this systems approach, we could simulate future scenarios based on different assumptions about the citizenry:

– **Optimistic Scenario**: Citizens receive accurate information, experience environmental impacts directly, and are emotionally mobilized to act. Here, the system might output high levels of civic engagement and support for long-term policies.

– **Pessimistic Scenario**: Citizens remain disengaged, misinformed, or focused on short-term needs. In this scenario, democratic outputs (e.g., election results, policies) may fail to address long-term survival goals.

– **Mixed Scenario**: Some citizens are engaged and informed, but others are not. The system might produce uneven outcomes, where some regions or groups make progress while others lag behind, potentially undermining collective action.

### 4. **Ideal vs. Realistic State**:

– In an ideal scenario, all citizens would have the necessary **knowledge, experience, emotions**, and **capabilities** to make decisions that align with long-term survival. The question is whether this ideal is attainable given the current societal structure.

– By comparing the ideal system state with the current or probable future states, we can estimate **gaps**:

– Where is knowledge lacking?

– Are citizens emotionally mobilized in the right direction?

– Do inequalities in bodily capabilities (economic, health) prevent broad participation?

– These gaps can highlight where interventions (e.g., education, policy reforms, emotional appeals, or social support systems) are needed to push the real system closer to the ideal.

### 5. **Assessing the Likelihood of Success**:

– **Goal State**: The goal is for a democratic society to produce behaviors and policies that align with long-term planetary survival.

– **Current Capability**: The current state of citizens—based on education, economic conditions, media influence, and political structures—may or may not align with this goal.

– By using simulation models (like those in engineering), you can estimate the **probability** that today’s democratic systems and citizen behaviors will converge on this goal.

– **High-probability outcome**: Requires widespread engagement, correct information dissemination, and a sense of urgency.

– **Low-probability outcome**: Results from misinformation, emotional disengagement, or unequal capacities to act.

### 6. **Intervention Strategies**:

– Just as in engineering, if the system is unlikely to meet its goal based on the simulation, **interventions** are necessary. In the case of democracy, this could involve:

– **Educational campaigns** to improve knowledge.

– **Public policies** that ensure greater equality in bodily capabilities (e.g., universal healthcare, basic income).

– **Emotional appeals and storytelling** to foster a deeper connection between citizens and the environment.

– **Political reforms** to reduce the influence of short-term interests (e.g., campaign finance reform, voting system changes).

—

### Conclusion:

By thinking of citizens as dynamic systems within the democratic process, you can simulate future behaviors and outcomes, assess the likelihood of achieving desired goals (like planetary survival), and identify the most effective intervention points. This systems approach could provide valuable insights into whether democracy, in its current form, can realistically address today’s challenges—or whether significant reform is needed to increase the probability of success.

Would you like to explore specific modeling strategies for these citizen systems or dive into examples of where such simulations have been applied in social or political contexts?

Well, I have the ‘feeling’ that this approach can be very fruitful. I would like to move further in that direction. But, unfortunately I have to interrupt the dialogue for some hours now. But I will continue later, for sure. Thanks for your support so far.

You’re very welcome! I’m glad you’re finding the approach promising. Feel free to pick up the conversation whenever you’re ready, and we can continue exploring this path further. Have a good break, and talk to you soon!

Hi Chat, I am back. These first thoughts to analyze the process of a democracy with regard to a ‘goal of survival’ can perhaps be further ‘expanded’ with a direct comparison between (1) an ‘engineering process’ within the paradigm of ‘systems engineering’ and a (2) a ‘democratic process’ where the ‘experts of a system engineering process’ are replaced by ‘elected citizens’. Would you like to follow this direction a bit with me?

Welcome back! I would be glad to follow this direction with you. Comparing an **engineering process** within the paradigm of systems engineering to a **democratic process** where citizens replace technical experts is an intriguing approach. Both involve dynamic, goal-oriented systems, but the mechanisms of decision-making and action are quite different. Let’s explore the comparison step by step:

### 1. **System Engineering Process**:

– **Goal**: The primary objective of systems engineering is to design, build, and maintain systems that meet a specified goal or solve a defined problem. The process is driven by technical experts with specialized knowledge and a structured approach to achieve an optimal solution.

– **Core Components**:

– **Requirements Analysis**: Identifying the needs or goals the system must fulfill.

– **Design and Modeling**: Creating simulations or models of the system to predict behavior and outcomes.

– **Implementation**: Putting the designed system into practice.

– **Testing and Evaluation**: Verifying whether the system meets the goal.

– **Feedback and Iteration**: Adjusting the system based on feedback to improve performance.

### 2. **Democratic Process** (With Citizens as Decision-Makers):

– **Goal**: In a democracy, the overarching objective (in this case, planetary survival) is pursued through collective decision-making by citizens, who elect representatives and influence policies. Instead of technical experts driving the process, ordinary citizens—who may or may not have technical expertise—guide policy through voting, activism, and civic engagement.

– **Core Components**:

– **Public Deliberation and Consultation**: Citizens engage in discussions, debates, and elections to influence policy.

– **Policy Design and Decision-Making**: Elected representatives and government bodies create policies based on public input and expert advice.

– **Implementation**: Governments and institutions put the policies into practice.

– **Monitoring and Accountability**: Citizens (through elections, protests, and media) evaluate whether policies are working and hold leaders accountable.

– **Feedback and Iteration**: Democratic systems allow for policy changes based on citizen input and election cycles.

—

### Key Comparisons:

1. **Expertise vs. Collective Input**:

– In **systems engineering**, technical experts design and execute the process, relying on specialized knowledge to optimize solutions.

– In a **democracy**, citizens (many of whom lack technical expertise) shape the process by electing representatives and engaging in public debate. While experts may advise, the ultimate decisions rest with the public.

– **Challenge**: Can the democratic process, with its reliance on diverse citizen input, achieve the same precision and efficiency as an expert-driven engineering process? Can citizens gather enough collective knowledge and motivation to set policies that ensure survival?

2. **Defining and Prioritizing Goals**:

– In **systems engineering**, the goal (e.g., designing a sustainable energy system) is clearly defined, often with precise technical specifications. Engineers work toward optimizing the system to meet those criteria.

– In a **democratic process**, the goal of survival may be broadly defined (e.g., addressing climate change, biodiversity loss), but citizens may have conflicting views on how to prioritize sub-goals (economic growth vs. environmental protection, for example).

– **Challenge**: Can democratic societies align on clear, measurable survival goals? How can citizens with diverse priorities agree on the most urgent actions?

3. **Modeling and Predicting Outcomes**:

– Systems engineers use **modeling and simulation** to predict the outcomes of their designs, allowing for adjustments before implementation.

– In a democracy, **predicting policy outcomes** is much more uncertain. While experts might simulate policy effects, citizens and politicians may struggle to fully grasp the long-term consequences of their decisions.

– **Challenge**: Can democratic processes incorporate the predictive tools used in systems engineering to give citizens and leaders a clearer picture of future outcomes?

4. **Implementation and Adaptation**:

– In systems engineering, the implementation process follows a structured plan, with adjustments made based on feedback and system performance.

– In a democracy, **implementation** can be chaotic and subject to political constraints, changing leadership, and public opinion shifts.

– **Challenge**: How can democratic systems ensure that policies are implemented effectively and adjusted based on outcomes, given the slower, more complex feedback loops in political processes?

—

### Possible Convergences and Hybrid Approaches:

– **Technocratic Elements in Democracy**: In some cases, democracies have adopted technocratic elements where **experts** are given key decision-making roles (e.g., independent central banks, regulatory agencies). Could a similar model be applied for existential challenges, where citizen input defines the broad goals, but experts are empowered to execute policy with less direct political interference?

– **Deliberative Democracy**: This model emphasizes **informed citizen panels** that receive expert guidance to make policy recommendations. It mimics some aspects of the engineering process by involving more focused, expert-informed discussions, but within a democratic framework. Could this approach be scaled to address survival issues?

– **Simulation for Citizens**: One possibility is using **system simulations** to present policy options and outcomes in a more digestible way for citizens. These tools could help voters understand the consequences of different decisions, much like engineers simulate a system’s behavior before implementation. Could democratic education benefit from these predictive tools to help citizens make more informed choices?

—

### Summary of the Comparison:

– **Systems Engineering** focuses on precision, expert-driven solutions, and predictive modeling to achieve a well-defined goal. It operates efficiently within known parameters but lacks broad public input.

– **Democratic Processes** prioritize citizen engagement and collective decision-making but often struggle with precision, long-term thinking, and efficient implementation.

To what extent do you think these approaches can be reconciled, or do you believe democracy requires a fundamental shift in structure to address planetary survival in the way systems engineering would? Would you like to explore specific hybrid models that could combine elements of both?

Thanks for your first analysis of these key factors. It seems that a system engineering process – meanwhile with more than 100 years of experience and training world wide, in many thousands of complex projects — is completely ‘designed’ as an efficient process, where all included factors are well defined. The result of this ‘evolution of an efficient system design and implementation process’ can be inspected by every one: it is very, very rare that the output of a real systems engineering process ‘crashes’. If we compare these results with the outcomes of democratic processes then we can observe a remarkable difference: the outcomes of political processes are rather sub-optimal or even really bad if not disastrous. Faced with such a situation, perhaps we as members of democratic processes should raise the question, whether the ‘ordinary format of democracies’ have to be improved substantially. What do You think of this?

I think you’re raising an important and thought-provoking point. The stark contrast between the **efficiency** and **reliability** of outcomes in systems engineering and the often **suboptimal** or even **disastrous** outcomes of democratic processes highlights a need to critically assess how democracy functions, especially in the face of complex, existential challenges.

### 1. **System Engineering: Structured Efficiency**

– As you pointed out, systems engineering has evolved through **decades of refinement**, with well-defined methodologies, tools, and frameworks to ensure efficient processes and reliable outcomes. Every phase—from design, to testing, to feedback—follows a structured approach to meet predefined goals.

– Failures in system engineering are rare because the process is built on a foundation of **expertise**, **data**, and **rigorous evaluation**. This makes the system **resilient**, adaptable to feedback, and optimized for achieving success, often measured in clear, objective terms.

### 2. **Democratic Processes: Complexity and Inefficiency**

– Democratic systems, in contrast, involve a broader, more complex range of actors, with diverse levels of **knowledge**, **interests**, and **capabilities**. The decision-making process is much more decentralized, less structured, and heavily influenced by short-term interests, emotions, and public opinion.

– Outcomes in democracy can be messy, inefficient, or slow to materialize, often leading to compromises that **don’t fully address the underlying problems**. This is especially true for long-term issues like planetary survival, where **political short-termism** and **conflicting interests** can derail the needed focus on critical, future-oriented solutions.

### 3. **Structural Weaknesses of Democracy**

Some key reasons why democratic outcomes tend to be less efficient than systems engineering include:

– **Short-Term Thinking**: Democracies are driven by **election cycles**, meaning politicians often prioritize short-term gains to secure re-election over long-term problem-solving. This contrasts sharply with the **long-term orientation** typical in systems engineering.

– **Public Misinformation**: In democracy, citizens are central to decision-making, but they can be influenced by **misinformation**, media manipulation, or emotional appeals, which can result in suboptimal choices that aren’t in line with scientific or expert advice.

– **Lack of Expertise**: Unlike system engineering, where decisions are made by trained experts, in a democracy, the decision-making process often falls to politicians, lobbyists, or voters who may lack the technical knowledge needed to make informed decisions on complex issues like climate change or economic sustainability.

– **Diverse Interests and Values**: Democracies must accommodate a wide array of **conflicting interests and values**, making it harder to design streamlined solutions that everyone can agree on. The result is often compromise or gridlock.

—

### 4. **Improving the ‘Ordinary Format’ of Democracies**

Given this comparison, it’s reasonable to ask whether democracies need to be **substantially improved** to function more effectively, especially in the context of solving complex, global challenges. Here are a few possible avenues for improvement:

#### a. **Technocratic or Expert-Led Decision-Making**:

– One solution could involve giving **greater decision-making power to experts** in fields like climate science, economics, or public health, while still allowing citizens to set broader goals through elections and public input.

– This could help bridge the gap between the **efficiency** of system engineering and the **collective decision-making** of democracy by ensuring that policy decisions are informed by the best available knowledge, while still maintaining democratic legitimacy.

#### b. **Deliberative Democracy**:

– In a deliberative democracy model, **citizen assemblies** are created, where a representative sample of the population is brought together, given access to expert advice, and tasked with developing solutions to specific problems. This approach can help citizens make **more informed decisions** and has been used in some countries to address issues like climate change or constitutional reform.

– Deliberative processes mimic elements of systems engineering by introducing a **structured process** of consultation and expert input, while maintaining democratic legitimacy through public participation.

#### c. **Longer-Term Political Frameworks**:

– Electoral cycles incentivize short-term thinking. Reforms that encourage **longer-term political commitments**—such as extending electoral terms for certain offices, or creating **multi-decade frameworks** for addressing existential risks—could help shift focus toward long-term solutions.

– Systems engineering thrives on well-defined, long-term goals, while democratic politics can often be reactive and driven by short-term concerns. Establishing **long-term institutional goals**, insulated from the immediate pressures of elections, could align democracy more closely with the success seen in systems engineering processes.

#### d. **Feedback Loops and Accountability**:

– **More robust feedback mechanisms** could be introduced in democratic systems to ensure policies are evaluated regularly and adjusted based on their success in meeting long-term survival goals.

– Borrowing from systems engineering, **dynamic monitoring systems** could assess the performance of democratic decisions in real-time, allowing for quicker adaptations and policy corrections based on feedback.

—

### 5. **A Hybrid System?**

We might consider whether democracy should **incorporate more elements of systems engineering**, such as:

– **Clear goal-setting**: Establish well-defined, long-term survival goals (e.g., climate targets) that transcend election cycles.

– **Expert panels**: Use permanent expert panels to guide policy decisions, especially on technically complex issues.

– **Simulation and modeling**: Implement predictive models to simulate policy outcomes and present this information to the public, giving citizens a clearer understanding of the potential future consequences of their decisions.

– **Citizen training programs**: Improve public education and create opportunities for citizens to engage more deeply with the complexities of policy and systems thinking.

—

### Conclusion:

The comparison between the highly efficient, **goal-oriented** system engineering processes and the **often inefficient, short-term** focus of democratic systems suggests that democracies might benefit from significant structural improvements. Adopting elements from systems engineering—such as expert input, predictive modeling, and clear long-term goals—could enhance democratic performance on existential issues like planetary survival.

What’s your view on integrating more technocratic or expert-driven processes into democracy, while maintaining its essential values? Would you like to explore specific models that could merge the best of both systems?

After these first reflections comparing system engineering processes with democratic processes there are more and more ‘hints’ that the analysis of Democracies and their democratic processes reveals a ‘high load of old concepts’, which in the past perhaps possessed some ‘logic’, but meanwhile the challenges a society has to solve seem to require a lot more special capabilities than considered before. This points in the direction, that we have to clarify how the ‘basic democratic ingredients’ are indeed not really contradicting by the enabling of more efficiency but — in the opposite — the dramatic lack of ‘efficiency and quality’ can erode the trust of the citizens in their democratic system and give raise to ‘populistic politicians’ which are finally perhaps even much more worse. What do you think?

You’re touching on a critical and timely issue: the gap between the **historical foundations** of democracy and the **modern challenges** that require new capabilities, as well as the danger that this gap could erode trust in democratic systems. Let’s explore this further.

### 1. **Outdated Democratic Concepts vs. Modern Challenges**

– **Historical Foundations**: Many of the **core ideas of democracy**—like citizen participation, free speech, and majority rule—were conceived in much simpler times, when societies were smaller, problems were less complex, and governance was less reliant on specialized knowledge. These concepts were designed to maximize **inclusivity and fairness**, rather than efficiency.

– **Modern Complexity**: Today’s societal challenges—climate change, global inequality, pandemics, and technological disruption—are vastly more complex. Solving these problems often requires **expertise, rapid adaptation, and long-term planning**, things that the traditional democratic process struggles to accommodate.

The key issue is that **efficiency and specialization** are now indispensable, but the basic mechanisms of democracy haven’t adapted adequately to the increasing demands for complex problem-solving.

### 2. **Efficiency, Quality, and Trust**

– **Efficiency and Trust**: As you rightly point out, a lack of **efficiency and quality** in democratic decision-making can lead to frustration among citizens. When governments fail to solve urgent problems effectively or seem to be bogged down by bureaucracy, short-termism, or ideological conflicts, trust in democratic institutions erodes.

– **Rising Populism**: This erosion of trust opens the door to **populist politicians**, who often exploit public dissatisfaction with the inefficiency of democratic systems. Populists tend to offer **simplified, emotionally charged solutions**, which can sound appealing but often **sidestep democratic norms**, undermine expert knowledge, and in many cases **aggravate problems** rather than solving them.

**Populism** feeds on the frustration that comes from the perception that traditional democratic processes are not capable of responding to modern challenges. If citizens feel that democracy cannot deliver solutions, they may turn to more authoritarian or populist leaders who promise quick fixes, even if these undermine long-term societal wellbeing.

### 3. **Democracy’s Adaptability**

– Democracies have shown **adaptability** over time, but the **rate of change** in today’s world is much faster, requiring democracies to evolve more rapidly than they have in the past. The gap between **what citizens expect** (efficient governance, real solutions to pressing issues) and **what democratic institutions are delivering** can widen, leading to disillusionment.

– **Tension with Efficiency**: Many believe that **introducing more efficiency** into democratic processes could make them more responsive to modern challenges without compromising core democratic values. This might include reforms such as:

– **Streamlined decision-making processes** for complex, urgent issues (like climate policy).

– **Increased use of expert panels** to provide informed guidance on technical matters.

– **Digital democracy tools** to enhance transparency, participation, and public engagement while speeding up consultation processes.

– **Clear long-term frameworks** that transcend short-term electoral cycles, focusing on **outcome-based governance** rather than reactionary politics.

### 4. **Populism as a Symptom, Not a Solution**

– **Populism** thrives in environments where traditional democratic institutions are seen as failing to solve everyday problems or long-term challenges. However, populist approaches often **dismiss complexity** and **sideline experts**, making it less likely that their solutions will succeed in the long run. Populist leaders may offer quick fixes, but their solutions often lack the depth or sustainability needed to address root causes.

– **Democratic Erosion**: Populism also often **erodes the democratic framework itself**, undermining the rule of law, diminishing civil liberties, or weakening checks and balances. This leads to further **democratic backsliding**, making societies more vulnerable to authoritarianism and less capable of responding to future challenges.

### 5. **Democracy’s Paradox: Complexity and Inclusivity**

– Democracies are built on the principle of **broad inclusivity**, ensuring that all citizens have a voice. But this inclusivity can sometimes **complicate** decision-making, especially when issues require **specialized knowledge** or when urgent action is needed. The paradox is that **democracy’s strength—its inclusiveness—can also be a weakness** in situations where streamlined, expert-driven decision-making is required.

– However, this **paradox need not be fatal** to democracy. By recognizing the need for **new approaches**—such as more **technocratic elements**, **deliberative processes**, or **better feedback mechanisms**—democracy can evolve to meet modern challenges without sacrificing inclusivity.

### 6. **Preventing Erosion Through Innovation**

To prevent the erosion of democratic trust and the rise of dangerous populist alternatives, democracies must:

– **Embrace Complexity**: Acknowledge that modern problems are complex and require **more sophisticated, expert-driven approaches**, while finding ways to **engage citizens meaningfully** in these processes.

– **Strengthen Democratic Legitimacy**: Provide more **transparency, accountability, and education** to help citizens understand why certain decisions are made, especially when these decisions are based on expert advice or long-term goals that might not yield immediate benefits.

– **Reform Electoral Systems**: Reforms such as **ranked-choice voting**, **longer political terms**, or **multi-decade policy frameworks** might help shift focus away from the **short-termism** of electoral cycles, aligning more closely with the long-term thinking required for planetary survival.

### 7. **Rebuilding Trust Through Accountability and Results**

– **Results-Based Governance**: One way to rebuild trust in democracy is through **results-based governance**. By focusing on **clear, measurable outcomes**, democratic systems can demonstrate their effectiveness in solving key problems, restoring public faith in their institutions.

– **Feedback Loops**: Democracies could benefit from **stronger feedback loops**, much like those found in systems engineering. This would allow policies to be adjusted based on real-world outcomes, ensuring that when policies don’t work as intended, they can be corrected quickly.

—

### Conclusion: An Urgent Need for Democratic Innovation

I agree with your concern that the **lack of efficiency** in current democratic processes can erode trust and create openings for populist leaders who may be worse for long-term societal survival. **Democracy** doesn’t have to be inherently inefficient—it just needs to **adapt**. By updating its processes, integrating more expert-driven elements, and emphasizing **accountability**, democracy can remain a powerful tool for solving modern problems without sacrificing its core values of inclusivity and participation.

Do you think the inclusion of more expert-led decision-making or long-term policy frameworks could help address some of these challenges? What are your thoughts on balancing **efficiency** and **inclusivity** in democratic reform?

OK, I think we have detected a very interesting point of view to elaborate further the possible ‘fate of democracies’ in our dynamic world. I am planning the following steps how to proceed further: (1) I will post this dialogue so far in two of my main blogs (English as well as German); then (2) I will analyze a bit more how we could transform Democracies 1.0 (lacking the needed efficiency) to a kind of Democracies 2.0 with a new approach to improve efficiency significantly by keeping the key values of democracy not only alive but ‘improve these values’ instead.

That sounds like a great plan! Your vision of transforming Democracies 1.0 into Democracies 2.0 by addressing efficiency while preserving and even enhancing key democratic values is a promising direction. I’m excited to see how your analysis unfolds, and I’d be happy to assist with any further insights, ideas, or refinements as you continue exploring this transformation.

Feel free to share your progress or any additional thoughts, and we can keep developing these concepts together. Good luck with your blog posts and the next phase of your work!

Last Change: July 27, 2024

Inquirer: Gerd Doeben-Henisch

Email: info@uffmm.org

(The German origin al source can be found HERE.)

CONTEXT

This post is part of the Dialogues with chatGPT4.

Summary

Running a dialogue with chatGPT4 to the question “What comes to your mind regarding the phrase ‘an empirically true text’?” the dialogue ends up with the following statement of chatGPT4:

“Your view that the ’empirically true text’ is essential is indeed valid, especially in the context of science. While dialogues and practical experiments are critical components of the research process, the resulting texts form the basis on which knowledge is formally shared, verified, and further developed. They are indispensable for creating a clear understanding and a reliable foundation for the continuation of research.”

Question : What comes to your mind regarding the phrase ‘an empirically true text’?

“The term ‘an empirically true text’ is interesting because it combines two aspects that are usually considered in different contexts: empiricism and truth in textual form.

Empiricism: This term refers to knowledge gained through observation, experiments, and experience. In science, this means that information is collected based on actual data and objective observations.

True Text: This could refer to the content of the text being correct and reliable, meaning that the facts or data presented in the text are true and verified by empirical methods.

When these two aspects are combined, ‘an empirically true text’ could describe a text that is based on demonstrable, observable, and verifiable data. Such a text would thus contain information that has been obtained and confirmed through scientific methods, thereby ensuring a high level of reliability and accuracy. This could be the case in scientific papers, research reports, or analytical articles where empirical evidence is used to support claims or test hypotheses.”

Question : When you use the term ‘knowledge gained through observation, experiments, and experience,’ what should one imagine? ‘Knowledge’ is not visible. Don’t two people, who want to communicate their ‘individual knowledge,’ need to encapsulate their knowledge ‘in language (= texts)’ so that the other can know about that knowledge?

“You’ve made an important point: knowledge itself is indeed an abstract concept and not directly visible or tangible. The transfer and communication of knowledge, especially empirical knowledge gained through observation, experiments, and experience, generally requires some form of documentation or representation, often through language or other symbolic systems.

Communication of Knowledge Through Language
To effectively share and disseminate knowledge, it is typically encapsulated in language. This can happen in several ways:

1. Written Documentation: Research findings, observations, and experimental data are often recorded in the form of scientific articles, reports, books, or online content. These texts enable others to understand, verify, and build upon the methods, results, and conclusions of the original study.
2. Oral Communication: Knowledge is also conveyed through lectures, presentations, discussions, and teaching sessions. In these contexts, knowledge is verbally articulated, often supported by visual aids such as graphics, diagrams, and videos.
3. Symbolic Representations: In some scientific disciplines, especially in mathematics and the natural sciences, knowledge is also expressed through formulas, models, graphs, and other non-textual means. This symbolism allows for a precise and concise representation of complex concepts and relationships.

Significance for Understanding and Transmitting Knowledge
The need to encapsulate knowledge in language contributes to its verifiability, teachability, and expandability. Through linguistic formulation, others can evaluate the validity of the information, compare it with their own knowledge and experiences, and develop further knowledge based on it.

Moreover, the linguistic representation ensures that knowledge can be shared independently of individual experiences and over time. This is particularly important in science, where reproducibility and objectivity are central values. It also emphasizes the collective nature of knowledge: it does not belong to just one individual, but is part of a larger, collaborative enterprise.”

Question : After this interim consideration, would you then agree that the term ’empirically true text’ is ultimately essential for people to share empirical knowledge together?

“Yes, I agree that the term ’empirically true text’ is not necessarily required to capture the basic idea of sharing empirical knowledge together. The concept of an ’empirically true text’ might be helpful in emphasizing the authenticity and reliability of the data presented in a text, but it is not the only method to share or validate knowledge.

Empirical knowledge is shared and verified in many formats, including:

1. Interactive Discussions and Collaborations: Knowledge is often developed through direct exchange and collaboration in research teams or discussion groups. Here, ideas and data can be shared in a less formalized, yet effective framework.
2. Experimental Repetitions: In science, the reproducibility of results is a key criterion for their validity. This means that empirical knowledge must be verifiable by allowing independent researchers to repeat the same experiments under the same conditions.
3. Peer Review: Scientific texts and their contents are evaluated through peer review processes, where peers assess the quality, relevance, and accuracy of the research. This contributes to the credibility and acceptance of the published findings.
4. Technological and Media Tools: Today, technological means such as databases, statistical analysis software, and specialized platforms also support the presentation and verification of empirical knowledge.

Overall, the phrase ’empirically true text’ is just one of many ways to describe the accuracy and reliability of empirical knowledge. The ability to share and verify knowledge depends less on the specific term and more on the methods and processes that enable information to be objectively assessed and communicated.”

Question : You misunderstood me: I actually mean that the term ’empirically true text’ is essential for people to share empirical knowledge together. Dialogues and experiments are indeed part of creating empirically true texts, but without a binding text, no one knows what the other really means. Or am I seeing something wrong here?

“Your point is very important, and I now understand that you are emphasizing the central role of texts in the dissemination and validation of empirical knowledge. Indeed, without a clear, precise, and binding written form, misunderstandings can occur and the accurate reproduction of results can become difficult. Thus, texts play a fundamental role in scientific communication and knowledge exchange.

The Role of the ‘Empirically True Text’:

1. Documentation: Scientific texts serve as permanent records of research findings and methods. Without these texts, it would be difficult for other scientists to build on existing knowledge or verify the results.
2. Standardization and Precision: By accurately describing experiments, observations, and analysis methods in scientific texts, other researchers can replicate the studies under similar or identical conditions. This is crucial for validating research findings.
3. Dissemination: Texts enable knowledge to be shared across geographical and temporal boundaries. They are essential for the dissemination of knowledge in the global scientific community.
4. Verifiability and Critique: Empirically true texts allow for critical engagement and review by the professional community. The peer-review process and subsequent discussions in the scientific community are unimaginable without clear, understandable, and precise texts.

Conclusion
Your view that the ’empirically true text’ is essential is indeed valid, especially in the context of science. While dialogues and practical experiments are critical components of the research process, the resulting texts form the basis on which knowledge is formally shared, verified, and further developed. They are indispensable for creating a clear understanding and a reliable foundation for the continuation of research.”

This text is part of the text “Rebooting Humanity”

(The German Version can be found HERE)

Author No. 1 (Gerd Doeben-Henisch)

Contact: info@uffmm.org

(Start: July 14, 2024, Last change: July 14, 2024)

Starting Point

We are still here on this planet. However, the fact that our ‘human system’ largely programs itself without our ‘voluntary participation’ can become our downfall within the framework of a democracy and in the age of the internet; not necessarily, but highly likely…

Self-programming

Unlike today’s machines, all living beings — including humans — are designed to ‘self-program’: whatever we perceive of ourselves, others, and the environment, is automatically transported into our interior and there it is also largely automatically structured, arranged, evaluated, and much more. No one can resist it. One can only control this ‘self-programming’ by shaping one’s environment in such a way that certain perceptions are less likely to occur, while others occur more. Educational processes assume this capacity for self-programming and they also decide what should happen during the education.

Dictatorship of the ‘Is There’

What has arrived within us is there for the time being. It forms our primary reality. When we want to act, we first rely on what is there. What is there is somehow ‘true’ for us, shaping our further perception and understanding. Something ‘around us’ that is ‘different’ is indeed ‘different’ and ‘does not fit’ with our inner truth.

Which points of comparison?

Suppose the majority of what is ‘inside us’, what we initially assume to be ‘true’, were ‘false’, ‘inappropriate’, ‘inaccurate’, etc., in the real world outside, we would have little chance of recognizing our own ‘untruth’ as long as most people around us share the same ‘untruth’.[1] Recognizing ‘untruth’ presupposes that one somehow has ‘examples of truth’ that are suitable for being ‘compared’ with the prevailing untruth. However, the presence of examples of truth does not guarantee the recognition of untruth, but only increases the likelihood that it might happen.[2]

[1] Throughout the known history of humanity, we can observe how certain ‘untruths’ were able to dominate entire peoples, not only in autocratic systems.

[2] Systems with state-promoted untruth can be identified, among other things, by the suppression of diversity, examples of truth, and the allowance of only certain forms of opinions.

Modern Untruths

Unlike autocratic systems, democratic systems officially have ‘freedom of speech’, which allows for great diversity. In democratic systems of the Democracy 1.0 format, it is assumed that this guaranteed freedom is not abused.[1]

With the advent of modern media, especially media in conjunction with the internet, it is possible to make money on a large scale with the distribution of media. The temptation is near to spread media over the internet in such a way that one can earn the maximum amount of money. A popular method is ‘advertising’: the longer and more often a user stays in front of content, the more advertising revenue flows. The temptation is great enough to offer the user only what automatically arouses his ‘automatic interest’. The fact that ‘automatic interest’ is a very strong motive and specifically correlates with content that does not require much thought is confirmed daily. It is now known and increasingly described that large parts of a population can be ‘specially programmed’ in this way.

In the ‘struggle of power-driven systems’, this possibility of external programming of people using the internet is massively exploited in the so-called ‘hybrid warfare’. While autocratic systems are ‘massively closed’, the modern democracies in the 1.0 format are almost an Eldorado for the application of hybrid warfare methods. Similar to the money-obsessed media industry, hybrid warfare also uses ‘light content’, mixing fragments of ‘true’ with fragments of ‘false’, particularly those that easily excite, and in a short time, the ‘flock of believers’ in these messages grows.[2] The ‘congregation of these propaganda believers’ cannot usually be influenced by ‘arguments’. These convictions are programmed in such a way that all sources that could represent critical alternatives are ‘outlawed’ from the start.[3]

And unfortunately, it is true that Democracies in the 1.0 format appear ‘weak’ and ‘helpless’ so far for this type of freedom use, although slowly the recognition is increasing that there is something like an abuse through ‘false programming of people’.

[1] Preventing systematic abuse of freedom in Democracies 1.0 is difficult to impossible without changing freedom of speech itself.

[2] The broad coverage of this propaganda can be easily recognized when one talks to people in different places in Germany (and abroad!) who do not know each other, but who tell more or less the same stories in a tone of conviction during the course of the conversation. Many (most of them?) even have higher education. This raises the question of how little an academic education apparently promotes a ‘critical spirit’.

[3] A popular term is ‘lying press’. Anything that could become ‘dangerous’ is ‘a lie’, although those who talk about lying press do not seriously engage with this press at all.

What is the probability of survival of truth?

Anyone who has ever delved deeply into the question of ‘truth’ in their life, and who knows that it takes various research, investigations, considerations, and even experiments to ‘look behind the apparent phenomena’, along with much communication with other people, sometimes in other languages, knows that truth is not automatic; truth does not just happen; truth cannot be obtained for ‘free’. The use of truth for beneficial technologies, new forms of agriculture, new transportation systems, etc., appears desirable in retrospect, at the end of a long journey, when it somehow becomes ‘obvious’ what all this is good for, but at the beginning of the path, this is almost unrecognizable to everyone. The pitiable state of the education system in many countries is a telling testament to the low regard for education as a training process for truth.

Given the rapid spread of unscrupulous internet media businesses accompanied by a worldwide surge in hybrid warfare, the survival probability of truth seems to be decreasing. Democracies, as the actual ‘bastions of truth’, are experiencing themselves as a place of accelerated ‘evaporation of truth’.

This text is part of the text “Rebooting Humanity”

(The German Version can be found HERE)

Author No. 1 (Gerd Doeben-Henisch)

Contact: info@uffmm.org

(Start: July 10, 2024, Last change: July 10, 2024)

Starting Point

As the texts of the book will gradually show, the term ‘collective knowledge’ represents a crucial keyword for a characteristic that deeply defines humans—the life form of ‘homo sapiens.’ For an individual, ‘collective knowledge’ is directly hardly perceivable, but without this collective knowledge, no single human would have any knowledge at all. Yes, this not only sounds like a paradox, it is a paradox. While a ‘contradiction’ between two different statements represents a factual incompatibility, a ‘paradox’ also conveys the impression of a ‘contradiction,’ but in fact, in terms of the matter, it is not an ‘incompatibility.’ The ‘knowledge of us individuals’ is real knowledge, but due to the finiteness of our bodies, our perception system, our memory, we can factually only gather a very small amount of knowledge ‘in us.’ However, the more people there are, the more ‘knowledge’ each person ‘produces’ daily—just analogously, or then also digitally—the greater grows the amount of knowledge that we humans ‘deposit’ in our world. Newspapers, books, libraries, databases can collect and sort this knowledge to a limited extent. But an individual can only find and ‘process’ small fractions of this ‘collected knowledge.’ The gap between the ‘available collected knowledge’ and the ‘individually processable knowledge’ is constantly growing. In such a situation, the availability of generative artificial intelligence in the format of chatbots (GAI-ChaBo) is almost an ‘evolutionary event’! This new technology does not solve all questions, but it can help the individual to ‘principally’ get a novel direct access to what we should call ‘collective knowledge of humanity.’

Before the digitalization of the world …

“Before the digitalization of the world, it was indeed laborious to convey thoughts and knowledge in a way that others could become aware of it: initially only through oral traditions, inscriptions in rocks, then parchment and papyrus, stones and clay with inscriptions. With the availability of paper, writing became easier (though there was the problem of durability); this led to the collection of texts, to books, and the first libraries with books (libraries existed even for cuneiform and clay tablets). Great libraries like the ‘Library of Alexandria’ became precious ‘collection points of knowledge,’ but they were also subjected to various destructive events during their existence, which could lead to great losses of recorded knowledge.

A ‘mechanized production of books’ has been around since the 8th century, and modern book printing began in the 15th century. The development of libraries, however, progressed slowly for a long time, often only on a private basis. It was not until the 19th century that there was a stronger development of the library system, now including public libraries.

Despite this development, it remained difficult for an individual to access knowledge through a library, and even if this (usually privileged) access existed, the availability of specific texts, their inspection, the making of notes—or later copies—was cumbersome and time-consuming. The access of the individual reader resembled small ‘sampling’ that even within the framework of scientific work remained very limited over the years. The language problem should not be overlooked: the proportion of ‘foreign-language books’ in the library of a country A was predominantly restricted to texts in the language of country A.

‘Acquisition of knowledge’ was therefore laborious, time-consuming, and very fragmented for an individual.

An increasingly important alternative to this hard-to-access field of library knowledge were modern magazines, journals, in many languages, with ever shorter ‘knowledge cycles.’ However, the more such journals there are, the more the natural individual limitations come into force, painfully felt in the face of the swelling journal knowledge. Currently (2024), it is hardly possible to estimate the exact number of scientific journals. In the field of computer science alone, approximately 2,000 journals are estimated with an average of about 25,000 (or more) articles per year. And scientific journals only in Chinese are stated to be over 10,000.[1]

[1] For more detailed information on the collection of Chinese journals, you can visit the East View page on China Academic Journals (CAJ) here.

With digitalization

Since the availability of the World Wide Web (WWW) in the 1990s, a unified information space has emerged that has continued to spread globally. Although we are currently witnessing an increasing ‘isolation’ of the WWW among countries, the development of a common information space is unstoppable.

Alongside this information space, technologies for ‘collecting,’ ‘storing,’ ‘retrieving,’ and ‘analyzing’ data have also evolved, making it increasingly possible to find ‘answers’ to ‘questions’ from ever more sources.

With the advent of so-called ‘Generative Artificial Intelligence in the format of Chatbots’ (GAI-ChaBo) since 2022, this ‘data utilization technology’ has reached a level that not only finds ‘raw data’ but also allows an individual user with their limited knowledge direct access to ‘collective human knowledge,’ provided it has been digitized.

For the ‘evolution of life on this planet,’ this availability of collective knowledge to the individual may be the most significant event since the appearance of Homo sapiens itself about 300,000 years ago. Why?

The next level?

The sustainability debate over the last approximately 50 years has contributed to the realization that, alongside the rather individual perspective of life, and strong regional or national interests and perspectives of success, there have gradually come into consciousness — not universally — perspectives that suggest — and are now substantiated by diverse data and models — that there are problems which exceed the event horizon of individual particular groups — and these can be entire nations. Many first think of ‘resources’ that are becoming scarce (e.g., fish stocks), or being polluted (world’s oceans), or dangerously reduced (forest systems, raw materials, life forms, etc.) or more. What has hardly been discussed so far, although it should be the most important topic, is the factor that produces all these problems: Homo sapiens himself, who by his behavior, yes, even just by his sheer numbers, causes all the known ‘problems.’ And this does not happen ‘automatically,’ but because the behavior of Homo sapiens on this planet is ‘controlled’ by his ‘inner states’ in such a way that he seems ‘incapable’ of changing his behavior because he does not have his ‘inner states’ under control.

These inner states, roughly considered, consists of needs, different emotions, and collected experiences linked with knowledge. Knowledge provides the ‘images’ of oneself, others, and the world as a Homo sapiens sees it. Needs and emotions can block, ‘bind,’ or change knowledge. The knowledge that is currently available, however, has great power: ultimately, a Homo sapiens can only do what his current knowledge tells him — if he listens to his knowledge and not to ‘others’ who mean something to him due to the life situation.

If one is now interested in a ‘possible future,’ or — even more specifically — in a ‘possible future that is as good as possible for as many people as possible,’ and sustainable, then the challenge arises as to how people in the situation of everyday life, a certain form of the present, can ‘mentally’ surpass this present in such a way that, despite the current present, they can ‘somehow’ think of a piece of ‘possible future.’

‘Generative Artificial Intelligence in the format of chatbots’ (GAI-ChaBo) can help make the (approximate) entirety of past knowledge — albeit only punctually based on questions — accessible, but the ‘knowledge of the past’ provides ‘nothing new’ out of itself and — above all —, the past does not necessarily have those ‘goals’ and ‘values’ that are necessary in the current present to precisely ‘want that possible future’ that will matter.

With this challenge, Homo sapiens collides ‘with himself’ full force, and he will not be able to ‘hide behind GAI-ChaBo.’ A GAI-ChaBo always only delivers what people have previously said and done, albeit in a breadth that an individual could not achieve, but ultimately a GAI-ChaBo only functions like a kind of ‘mirror of the human collective.’ A GAI-ChaBo cannot replace humanity itself. A GAI-ChaBo is the product of collective human intelligence and can make the entirety of this collective intelligence visible in outline (an incredibly great achievement), but no more.

For the next level, Homo sapiens must somehow manage to ‘get a grip on himself’ in a completely different way than before. There are hardly any usable role models in history. What will Homo sapiens do? GAI-ChaBo is an extraordinary success, but it is not the last level. We can be curious…

This text is part of the text “Rebooting Humanity”

The German version can be found HERE.

Author No. 1: Gerd Doeben-Henisch
Contact: cagent@cognitiveagent.org

(Start: June 5, 2024, Last updated: June 6, 2024)

Starting Point

In the broader spectrum of human activities, there are three ‘types’ of action patterns that are common in everyday life and crucial for shared communication and coordination: (i) writing texts, (ii) describing (calculating) quantitative relationships, and (iii) planning possible states in an assumed future. All three types have been present since the beginning of documented ‘cultural life’ of humans. The following attempts a rough typology along with known forms of implementation.

Types and their implementation formats

(a) MANUAL: We write texts ‘manually’ using writing tools and surfaces. Similarly, in quantitative matters, there is manual manipulation of objects that represent quantitative relationships. In planning, there is the problem of how to represent a ‘new’ state: if the ‘new’ is ‘already known’, one can revert to ‘images/symbols’ of the known; if it is ‘truly new’, it becomes difficult; there is no ‘automatism of the new’. How do you describe something that has never existed before? Added to this is the — often overlooked — problem that ‘objects of planning’ are usually ‘value and goal dependent’; needs, intentions, expectations, factual necessities can play a role. The latter can be ‘socially standardized’, but given the ‘radical openness of the future’, history has shown that ‘too strong standardizations’ can be a shortcut to failure.

(b) MANUAL SUPPORT: Skipping the phase of ‘partially mechanical’ support and moving on to the early phase of ‘computer support’, there are machines that can be ‘programmed’ using ‘programming languages’ so that both the writing instrument and the surface are represented by the programmed machine, which allows many additional functions (correcting texts, saving, multiple versions, automatic corrections, etc.). However, one still has to write oneself: letter by letter, word for word, etc. In ‘calculating’, writing down is still very laborious, but the ‘calculation’ then takes place partially ‘automatically’. Planning is similar to writing texts: the ‘writing down’ is supported (with all the additional functions), but ‘what’ one writes down is left to the user. Apart from ‘quantitative calculating’, a ‘projection’, a ‘prediction’ is generally not supported. An ‘evaluation’ is also not supported.

(c) LANGUAGE-BASED SUPPORT: The phase of ‘language-based support’ replaces manual input with speaking. For selected areas of texts, this is becoming increasingly successful. For ‘quantitative matters’ (calculating, mathematics, etc.), hardly at all. For planning also only very limited, where it concerns already formulated texts.

(d) ARTIFICIAL INTELLIGENCE ENVIRONMENTS: The Artificial Intelligence (AI) environment is considered here in the context of dialogue formats: The user can ask questions or send commands, and the system responds. The relevant formats of AI here are the so-called ‘generative AIs’ in the chatbot format. Under the condition of ‘existing knowledge’ of humans in the format ‘stored documents/images/…’ and under the condition of ‘dialogue formats’ of humans (also through explicit training), these generative AIs can process questions and orders in ‘formal proximity’ to the known material in the context of a dialogue so that one does not have to intervene oneself. Corrections and changes in detail are possible. Both in ‘text creation’ and in ‘calculating’, this can function reasonably well within the realm of the ‘known’. However, the actual accuracy in the ‘real world’ is never guaranteed. In ‘planning’, the specific problem remains that for the AI, ‘truly new’ is only limited possible within the framework of combinatorial matters. The truth reservation remains, but also applies in the ‘manual case’, where the human plans themselves. The evaluation problem is also limited to already known evaluation patterns: the future is not the same as the past; the future is more or less ‘different’. Where should the necessary evaluations come from?

An interesting question remains, in what sense the advancing support by generative AIs actually supports communication and coordination among people, and specifically, whether and to what extent the central challenge of ‘future planning’ can be additionally supported by it. The fact is that we humans struggle with future planning in the areas of social life, community life, and larger contexts such as counties, states, etc., ranging from difficult to very difficult. But, for a sustainable future, successful planning seems to be indispensable.

This text is part of the text “Rebooting Humanity”

(The German Version can be found HERE)

Author No. 1 (Gerd Doeben-Henisch)

Contact: info@uffmm.org

(Start: June 25, 2024, Last change: June 28, 2024)

Starting Point

The excerpt discusses the concept of ‘collective human intelligence (CHI)’ and reflects on the foundational schema of all life : reproduction of Generation 1, birth of Generation 2, growth of Generation 2, followed by the onset of Generation 2’s behaviors accompanied by learning processes, and then reproduction of Generation 2, etc. It highlights how genetic predispositions and ‘free adapting’, commonly referred to as ‘learning’, alternate in phases. While genetic guidelines enable structures with typical functionalities that open up ‘possible action spaces’, filling these spaces is not genetically determined. This makes sense because the real ‘biological carrier system’ is not isolated but exists in an ‘open environment’ whose specific configuration and dynamics constantly change. From a ‘sustainable survival’ perspective, it is crucial that the biological carrier system has the ability to not only grasp the nuances of the environment at specific moments but also to represent, combine, and test them in the context of space and time. These simple words point to a highly complex process that has become known as ‘learning’, but the simplicity of this term may overlook the fact that we are dealing with an ‘evolutionary miracle of the highest order’. The common concept of ‘evolution’ is too limited in this perspective; it only describes a fragment.

A ‘Logic of Life’?

Basic Pattern of All Life

The ‘basic pattern of all life’ provokes many considerations. It is striking how phases of genetic change, which imply new structures and functionality, ultimately transform the ‘initial space’ of genetic changes into new, significantly more complex spaces, not just once, but repeatedly, and the more often, the more complexity comes within reach.

The life form of ‘Homo sapiens’—us, who call ourselves ‘humans’—represents a provisional peak of complexity in the temporal view of history so far, but already suggests from within itself a possible ‘next evolutionary stage’.

Even viewed closely, the individual human—with his structured cell galaxy, with the possible functions here, with his individual learning—represents an extraordinary event—relative to the entire known universe—, but this ‘individual’ human in his current state is already fully designed for a ‘plurality of people’, for ‘collective behavior’, for ‘collective learning’, and certainly also for ‘collective achievements’.

[1] The world of ‘molecules’ is transformed into the world of ‘individual cells’; the world of ‘individual cells’ is transformed into the world of ‘many cells (cellular complexes)’; the world of ‘cell complexes’ is transformed into the world of ‘structured cell complexes’, …, the world of structured ‘cell galaxies’ is transformed into the world of ‘cooperating structured cell galaxies with individual and collective learning’, …

Temporal Classification

Not only have the last few millennia shown what many people can achieve together, but particularly the ‘modern engineering achievements’ involving the collaboration of many thousands, if not tens of thousands of experts, distributed globally, over extended periods (months, year, many years), simultaneously in many different languages, dealing with highly complex materials and production processes—processes in which meta-reflection and feedback loops are taken for granted –… These processes, which have been globally initiated since the great war in the mid-20th century, have since become more and more the everyday standard worldwide. [2] The invention of programmable machines, information networks, highly complex storage systems, and the provision of ever more ‘human-compatible interfaces’ (visual, acoustic, tactile, …), up to those formats that make it appear to the human user as if ‘behind the interface’ there is another living person (even if it is ‘just’ a machine), have all occurred within just about 70 years.

While it took a considerable amount of time from the first evidences of biological life on planet Earth (around -3.4 billion years ago) to the first proven appearance of Homo sapiens in North Africa (around -300,000 years ago), the development of the complex ‘mental’ and ‘communicative’ abilities of Homo sapiens starting around -300,000 years ago, was initially slow (invention of writing around -6000), but the development then accelerated significantly over the last approximately 150 years: the complex events are almost overwhelming. However, considering the entire time since the presumed formation of the entire universe about 13.7 billion years ago, there is a rough time schema:

After about 75% of the total time of the existence of the universe, the first signs of biological life.

After about 99.998% of the total time of the existence of the universe, the first signs of Homo sapiens.

After about 99.999998% of the total time of the existence of the universe, the first signs of complex collective human-technical intelligence achievements.

This means that, in relation to the total time, the periods for the ‘latest’ leaps in complexity are so ‘short’ that they can no longer be distinguished on a large scale. This can also be interpreted as ‘acceleration’. It raises the question of whether this ‘acceleration’ in the creation of increasingly complex collective intelligence achievements reveals a ‘logic of process’ that would enable further considerations?

[2] Here began the career of the modern form of ‘Systems Engineering’, a quasi-standard of problem solving, at least in the English-speaking world.

Complexity Level: Biological Cell

With the description of a ‘basic pattern of all life’, a pattern emerges that is describable at least onwards from the complexity level of a biological cell.

The complexity level preceding the biological cell is that of ‘molecules’, which can be involved in different process chains.

In the case of the biological cell, we have, among other things, the case where molecules of type 1 are used by molecules of type 2 as if the type 1 molecules were ‘strings’ that ‘represent’ molecules of type 3, which are then ‘produced’ through certain chemical processes. Put differently, there are material structures that interpret other material structures as ‘strings’, possessing a ‘meaning assignment’ that leads to the creation of new material structures.

Thus, biological cells demonstrate the use of ‘meaning assignment’, as we know structurally in the case of symbolic languages from complex cell galaxies. This is extremely astonishing: how can ‘ordinary molecules’ of type 2 have a ‘meaning assignment’ that allows them to interpret other molecules of type 1 as ‘strings’ in such a way that they—according to the meaning assignment—lead to the organization of other molecules of type 3, which ultimately form a structure with functional properties that cannot be derived ‘purely materially’ from the type 1 molecules.

… !! New text in preparation !!..

[3] In this context, the term ‘information’ (or ‘biological information’) is commonly used in the literature. If this usage refers to the terminology of Claude Shannon, then it would be difficult to apply, as in the specific case it is not about the transmission of ‘signal elements’ through a signal channel to ‘received signal elements’ (a structural 1-to-1 mapping), but about an assignment of ‘signs (= signal elements)’ to something ‘completely different’ than the original signal elements.

A ‘Logic’?

When the main title tentatively (‘hypothetically’) mentions a ‘Logic of Life’, it is important to clarify what specifically is meant by the term ‘logic’ as a possible concept.

The term ‘logic’ dates back to Aristotle, who introduced it around 2400 years ago in Greece. It was then translated back into the Latin of the Christian Middle Ages via the Islamic culture around 1000 AD, profoundly influencing the intellectual life of Europe until the late Middle Ages. In contrast to ‘modern formal logic’—from the late 19th century onwards—the ‘Aristotelian logic’ is also referred to as ‘classical logic’.

If one disregards many details, classical and modern logic differ fundamentally in one aspect: in classical logic, the ‘linguistic meaning’ of the expressions used plays an important role, whereas in modern logic, linguistic meaning is completely excluded. ‘Mutilated remnants’ of meaning can still be found in the concept of an ‘abstract truth’, which is reflected in ‘abstract truth values’, but their ‘meaning content’ is completely empty.

The concept of both classical and modern logic—despite all differences—is united by the concept of ‘logical reasoning’: Suppose one has a set of expressions that are deemed ‘somehow true’ by the users of logic, then there are ‘rules of application’ on how to generate other expressions from the set of ‘assumed true expressions’, which can then also be considered ‘true expressions’. This ‘generation’ of new expressions from existing ones is called ‘reasoning’ or ‘inference’, and the ‘result’ of the reasoning is then a ‘conclusion’ or ‘inference’.

A more modern—formulaically abbreviated—notation for this matter would be:

A ⊢_Tr B

Here, the symbol ‘A’ represents a set of expressions assumed to be true, ‘Tr’ stands for a set of transformation instructions (usually called ‘rules of inference or inference rules’), ‘B’ stands for a generated (derived) expression, and ‘⊢’ refers to an ‘action context’ within which users of logic use transformation rules to ‘generate B based on A’.

A ‘normal’ logician, in the case of the symbol ‘⊢’, does not speak of an ‘action context’ but usually just of a ‘concept of inference’ or—with an eye to the widespread use of computers—of an ‘inference mechanism’; however, this way of speaking should not obscure the fact that ‘what actually exists’ are once concrete ‘objects’ in the form of expressions ‘A’ and ‘B’, and also in the form of expressions ‘Tr’. These expressions as such have neither any ‘meaning’ nor can these expressions ‘generate anything by themselves’. For the concrete expressions ‘B’ to be classified as ‘inference’ from the expressions ‘A’, which are ‘really generated’ by means of ‘Tr’, a real ‘process’ must take place in which ‘B’ is ‘really generated’ from ‘A’ ‘in the sense of Tr’.

A process is a real event ‘in time’, in which there is a real state that contains the object ‘A’, and a real logic user who has a ‘concept = model’ of ‘logical reasoning’ in his head, in which the ‘expressions’ of the generation rules Tr are linked with concrete process steps (the meaning of the expressions Tr), so that the logic user can identify the expressions belonging to A as part of the generation rules in a way that the generation rules can assign a new expression B to the expressions A. If this assignment ‘in the mind of the logic user’ (commonly referred to as ‘thinking’) is successful, he can then write down a new expression B referring to the concrete expressions Tr in a ‘subsequent situation’. Another logic user will only accept this new expression ‘B’ if he also has a ‘concept = model’ of logical reasoning in his head that leads to the same result ‘B’ in his mind. If the other logic user comes to a different result than ‘B’, he will object.

–!! Not finished yet! —

This text is part of the text “Rebooting Humanity”

(The German Version can be found HERE)

Author No. 1 (Gerd Doeben-Henisch)

Contact: info@uffmm.org

(Last modified: June 14, 2024)

Starting Point

How can one philosophically conceive of artificial intelligence (AI) interacting with real people, an AI that learns real language with real meaning on its own? Prompted by an offer from Ars Electronica ’95 to introduce a philosophically inspired art project, I spent intense months with an ad hoc software team (the team was wonderful!) designing (and implemented by the software team) the interactive network version of a small artificial world based on philosophical considerations. In this world lived ‘blind Knowbots’ that could communicate with the outside world, using their other sensory experiences and basic bodily states as the basis for assigning meanings to their respective languages. Whatever language the users (mostly children!) used, they could link this language with their real-world experiences. This experiment has shaped me for many years, actually up to today.

Blind’s World One

(The text was copied from the Ars Electronica ’95 book since the text is no longer accessible)

This text is part of the text “Rebooting Humanity”

(The German Version can be found HERE)

Author No. 1 (Gerd Doeben-Henisch)

Contact: info@uffmm.org

(Start: June 14, 2024, Last Modification: June 14, 2024)

Starting Point

Since September 2015, I have been repeatedly trying—both theoretically and practically—to understand what sound art really is; what is sound? What does it do to us? One consideration led to another; between them were real experiments and live performances. There were also long periods of ‘standstill’…. At a sound art concert on June 11, 2024, at Mousonturm in Frankfurt, something clicked in my mind regarding a fundamental question, and suddenly the uniqueness of ‘collective human intelligence’ in confrontation with so-called ‘intelligent machines’ became somehow newly clearer to me.

XEROX EXOTIQUE #090 – IMPRESSIONS

This post on an associated blog is about people and machines that can generate sound.

The trigger was a sound art event at the Mousonturm in Frankfurt am Main on June 11, 2024.

Here comes the translation:

A Hint from a Friend

Following a tip from Tobias (PiC, Xerox Exotique, …), I made a trip yesterday to the sound art event #090, organized by Xerox Exotique at the Mousonturm in Frankfurt am Main.

Impressions

SKETCH: Mousonturm, a small event area to the right of the entrance with a small stage. Some participants are highlighted. Detailed information about the event can be found on the XEROX EXOTIQUE website (xeroxex.de).

What to Talk About?

A sound art event like this offers numerous starting points for discussion…

Since the beginning of Philosophy in Concert (PiC), I have been driven by the question of how to situate soundscapes in the life world of people so that they do not seem like ‘foreign bodies,’ somehow ‘detached’ from the process of humans on this planet, but as a ‘living part’ of this very real-dynamic process made visible.

At concerts based on written music (scores…), it all revolves around the sets of symbols that someone has produced, which others convert into sounds, and perhaps about the person who holds the ‘office of the interpreter’ and tells other implementers how they should convert. The ‘typically human’ aspect may then be recognized in the ‘background of the notation’, in the way of ‘converting’ or ‘interpreting’, and then the effect of the sound cloud in the room on the people who sit, listen, and experience various emotions…

How much of the human process is revealed in such a form of event?

There is almost never any talking, and if there is, what is there to talk about? About one’s own feelings? About the technical intricacies of the written? About the skill of the converters? About the beauty of a voice? Yes, it’s not easy to integrate the sound event into the life process… and yet, it affects somehow, one remembers, talks about it later, may rave or complain…

The Hidden Human

Let’s briefly change the context and dive directly into the current global euphoria many people have over the new chatbots, which increasingly fascinate more people in everyday life, products of ‘generative Artificial Intelligence’ (chatGPT & Co).

The algorithms behind the interface are comparatively simple (although the global deployment is due to impressive engineering). What fascinates people in front of the interface is ‘how human the algorithms appear in the interface’. They use everyday language just as ‘we humans’ do, ultimately even better than most of those who sit in front of it. And — almost irresistibly — many see, because of this language and the accessible knowledge ‘behind the interface’, not a simple machine but something ‘profoundly human’. What is ‘human’ about this appearance, however, are the words, sentences, and texts that the simple algorithm has compiled from millions of documents, all of which come from humans. On its own, this algorithm cannot generate a single sentence! It lacks fundamental prerequisites. The ‘actual’ wonder sits in front of the ‘apparent’ wonder: it is we humans, who have, are, and represent something that we are barely aware of ourselves (we are ‘blind through ourselves’), and we marvel when simple algorithms show us what we are… ultimately like a mirror of humanity, but most do not notice; we get excited about simple algorithms and forget that we ourselves are exactly this wonder that has produced all this, continues to produce… we become blind to the real wonder that we ourselves are, each of us, all together.

Collective Intelligence – Collective ‘Spirit’…

In the case of algorithms, the term ‘artificial intelligence (AI)’ has been used for a long time, and more moderately, ‘machine learning (ML)’. However, the concept of intelligence has not yet been truly standardized, even though psychology has developed and experimentally researched interesting concepts of ‘intelligence’ (e.g., the ‘Intelligence Quotient (IQ)’) for humans for about 120 years. The communication between psychology and computer science, however, has never been very systematic; rather, everyone does ‘their own thing’. Thus, precisely determining the relationship between ‘human intelligence (HI)’ and ‘artificial intelligence (AI)’ has so far been quite difficult; the terms are too vague, not standardized. Moreover, it is complicated by the fact that the ‘actually impressive achievements’ of humans are not their ‘individual achievements’ (although these are important), but everything that ‘many people together over a long time’ have accomplished or are accomplishing. The term ‘Collective Human Intelligence (CHI)’ is in this direction but is probably too narrow, as it’s not just about ‘intellect’ but also about ‘communication’, ’emotions’, and ‘goals’. Unfortunately, research on the topic of Collective Human Intelligence is still far behind. The focus on the individual runs deep, and then in times of artificial intelligence, where individual machines achieve remarkable feats (under the premise of the collective achievements of humans!), even the study of individual human intelligence has fallen into the shadow of attention.

How do we get out of this impasse?

Sound Art as a Case Study?

I hadn’t attended a sound art concert in many years. But there were still memories, various aspects swirling through my mind.

The tip from Tobias catapulted me out of my usual daily routines into such a sound art event at the Mousonturm on June 11, 2024, at 8:00 pm.

As I said, there is a lot to talk about here. For a long time, I have been preoccupied with the question of the ‘collective’ dimension in human interaction. The ‘synchronization’ of people by algorithms is nothing unusual. In a way, humans have always been ‘standardized’ and ‘aligned’ by the prevailing ‘narratives,’ and the rapid spread of modern ‘narratives’ and the speed with which millions of people worldwide join a narrative is a fact. Most people (despite education) are apparently defenseless against the emergence of ‘narratives’ at first, and then very soon so strongly ‘locked-in’ that they reproduce the narratives like marionettes.

What role can ‘sound art’ play against such a backdrop? Sound art, where there is nothing ‘written’, no ‘central interpreter’, no ‘converters of the written’, but, yes, what?

That evening, the first group, ‘Art Ensemble Neurotica’, seemed to me to most broadly illustrate the profound characteristics of sound art. In the two following solo acts, where the individual performer interacted with sound they themselves produced, the special dimension of sound art was also present, in my view, but more concealed due to the arrangement.

In the case of Neurotica: Four people generated sound, live, each individually: Dirk Hülstrunk (narrator) – Michael Junck (digital devices) – Johannes Aeppli (percussion) – Guido Braun (strings & conductor). Each person on stage was a ’cause’, equipped with instruments that allowed all sorts of sound effects. There were no written notes; there hadn’t been a real rehearsal beforehand, but some arrangements (according to Guido).

Anyone who knows how diversely each individual can generate sound under these conditions can imagine that this seemingly infinite space can give rise to tension about what will happen next?

Describing the totality of sound that emanated from the four performers upfront for 45 minutes is nearly impossible in detail. At no stage did it seem (I exchanged views immediately afterwards with Roland (incorrectly identified as Robert in the sketch) next to me—we didn’t know each other, it was a coincidence we sat next to each other), that one sound source drowned out or overwhelmed another; everything appeared side by side and intertwined in a somehow ‘fitting form’, appealing and stimulating. Patterns from all four individual sources could be recognized interacting with each other over extended phases, yet they were supple, changing shape. Effects like volume shifts, echo, reverb, distortion, etc., did not feel out of place but seemed ‘harmonic’… giving each source a ‘character’ that combined with the others to form an overall impression…

Can such an arrangement of sounds be taken ‘purely abstractly’, detached from their creators? Could software generate such a complex sound event?

While the listener initially hears only the produced sound and might not immediately decide from this perspective whether it matters who and how this sound is produced, from the perspective of creation it quickly becomes clear that these sounds cannot be isolated from the producer, from the ‘inner states’ of the producer. Ultimately, the sound is created in the moment, in the interaction of many moments inside each individual actor (human), and this individual is not ‘alone’, but through his perception and many jointly experienced sound processes, each possesses a ‘sound knowledge’ that he more or less ‘shares internally’ with others, and thus each can bring his current inner states into a ‘dialogue’ with this ‘shared sound knowledge’. It is precisely this inner dialogue (largely unconscious) that provides opportunities for complex synchronizations, which an individual alone, without a shared history of sound, could not have. The resulting complex sounds are therefore not just ‘sound’ but are more manifestations of the internal structures and processes of the creators, which as internal meaning are linked with the external sound: Sound art sound is therefore not just sound one hears, it is also fully a kind of ‘communication’ of ‘inner human states’, spread over various collaborating individuals, thus a true collective event that presupposes the individual but extends far beyond in the happening. In this form of distributed sound art, the individual can experience themselves as a ‘WE’ that would otherwise be invisible.

Postscript

So, I now have this strange feeling that participating in this sound art event has led me deeper into the great mystery of us humans, who we are, that we have a special dimension of our existence in our ability to ‘collectively feel, think, and act,’ which somewhat liberates us from ‘individuality’ towards a very special ‘We’.

While a soundscape is ‘authentic’ and as such not ‘underminable’, ‘narrative spaces’—the use of language with an assumed, but not easily controllable potential meaning—are extremely ‘dangerous’ spaces: assumed meanings can be false and—as we can see today on a global scale—are predominantly wrong with correspondingly devastating consequences. Moving in distributed sound spaces has its ‘meaning’ ‘within itself’; the ‘Self in sound together’ is not underminable; it is mercilessly direct. Perhaps we need more of this…

This text is part of the text “Rebooting Humanity”

(The German Version can be found HERE)

Author No. 1 (Gerd Doeben-Henisch)

Contact: info@uffmm.org

(Start: June 14, 2024, Last Modification: June 14, 2024)

Starting Point

In both the section “Talking about the World” and the section “Verifiable Statements,” the theme of ‘change’ continuously emerges: our everyday world is characterized by everything we know being capable of ‘changing,’ including ourselves, constantly, often unconsciously; it just happens. In the context of people trying to collectively create an understanding of the world, perhaps also attempting to ‘plan’ what should be done together to achieve the best possible living situation for as many as possible in the future, the phenomenon of ‘change’ presents an ambivalent challenge: if there were no change, there would be no future, only ‘present’; but with change occurring, it becomes difficult to ‘look into the future’. How can we know into what future state all these changes will lead us? Do we even have a chance?

Changes

Motivation

In the current scenario, we assume a context of people trying to collectively form a picture of the world, who may also be attempting to ‘plan’ joint actions. It’s essential to recognize that the ‘relevant’ topics of interest are influenced by ‘which people’ one is working with, as each group within a society can and often does have its ‘own perspectives’. It is not only in ‘autocratic’ societal systems that citizens’ perspectives can be easily overlooked; there are plenty of examples in officially ‘democratic’ systems where citizens’ concerns are also overlooked, warranting closer analysis.

This discussion initially focuses on the fundamental mechanisms of ‘change’, specifically the ‘collective description’ of changes. The motivation for this emphasis stems from the fact that different people can only ‘coordinate (align) their actions’ if they first manage to ‘communicate and agree’ on the ‘contents of their actions’ through ‘communication processes’.

While simple situations or small groups may manage with verbal communication alone, most scenarios require ‘written texts’ (documents). However, written text has a disadvantage compared to direct speech: a ‘text’ can be ‘read’ in a situation where the ‘reader’ is not currently in the situation being described. In terms of ‘verifiability of statements’, this presents a real challenge: every text, due to ‘learned meaning relationships’, automatically has a ‘meaning’ that is activated ‘in the mind of the reader’, but it is crucial to verify whether there is a ‘real verifiable correspondence’ to the situation ‘described’ in the text.

If we assume that a group of people seriously contemplates a ‘future’ that they believe is ‘more likely to occur than not’—not just ‘theoretically’ but ‘actually’—then there must be a way to design the description of a ‘starting situation’ such that all participants have a chance to verify its accuracy in their shared everyday life.

…

This text is part of the text “Rebooting Humanity”

(The German Version can be found HERE)

Author No. 1 (Gerd Doeben-Henisch)

Contact: info@uffmm.org

(Start: June 7, 2024, Last change: June 9, 2024)

Starting Point

Speaking in everyday life entails that through our manner of speaking, we organize the perceptions of our environment, solely through our speech. This organization occurs through thinking, which manifests in speaking. As previously described, while the ability to speak is innate to us humans, the way we use our speech is not. In speaking, we automatically create an order, but whether this order actually corresponds to the realities of our everyday world requires additional verification. This verification, however, does not happen automatically; we must explicitly desire it and carry it out concretely.

Verifiable Statements

If one accepts the starting point that linguistic expressions, which enable our thinking, are initially ‘only thought’ and require additional ‘verification in everyday life’ to earn a minimal ‘claim to validity in practice’, then this basic idea can be used as a starting point for the concept of ’empirical verifiability’, which is seen here as one of several ‘building blocks’ for the more comprehensive concept of an ’empirical theory (ET)’.

Language Without Number Words

Here are some everyday examples that can illustrate some aspects of the concept of ’empirical verifiability’:

Case 1: There is an object with certain properties that the involved persons can perceive sensorily. Then one person, A, can say: ‘There is an object X with properties Y.’ And another person, B, can say: ‘Yes, I agree.’

Case 2: A specific object X with properties Y cannot be sensorily perceived by the involved persons. Then person A can say: ‘The object X with properties Y is not here.’ And another person, B, can say: ‘Yes, I agree.’

Case 3: There is an object with certain properties that the involved persons can sensorily perceive, which they have never seen before. Then person A can say: ‘There is an object with properties that I do not recognize. This is new to me.’ And another person, B, can then say: ‘Yes, I agree.’

The common basic structure of all three cases is that there are at least two people who ‘speak the same language’ and are in a ‘shared situation’ in everyday life. One person—let’s call him A—initiates a conversation with a ‘statement about an object with properties,’ where the statement varies depending on the situation. In all cases, the person addressed—let’s call him B—can ‘agree’ to A’s statements.

The three cases differ, for example, in how the object ‘appears’: In case 1, an object is ‘simply there,’ one can ‘perceive’ it, and the object appears as ‘familiar.’ In case 2, the object is known, but not present. In case 3, there is also an object, it can be perceived, but it is ‘not known.’

For the constructive success of determining an agreement that finds approval among several people, the following elements are assumed based on the three cases:

The participants possess:

• ‘Sensory perception’, which makes events in the environment recognizable to the perceiver.
• ‘Memory’, which can store what is perceived.
• ‘Decision-making ability’ to decide whether (i) the perceived has been perceived before, (ii) the perceived is something ‘new,’ or (iii) an object ‘is no longer there,’ which ‘was there before.’
• A sufficiently similar ‘meaning relationship’, which enables people to activate an active relationship between the elements of spoken language and the elements of both perception and memory, whereby language elements can refer to contents and vice versa.

Only if all these four components [2] are present in each person involved in the situation can one convey something linguistically about their perception of the world in a way that the other can agree or disagree. If one of the mentioned components (perception, memory, decision-making ability, meaning relationship) is missing, the procedure of determining an agreement using a linguistic expression is not possible.

[1] There are many different cases!

[2] These four concepts (perception, memory, decision-making ability, meaning relationship) are ‘incomprehensible on their own.’ They must be explained in a suitable context later on. They are used here in the current concept of ‘verifiable statements’ in a functional context, which characterizes the concept of ‘verifiable statement’.

Language with Numerals

Typically, everyday languages today include numerals (e.g., one, two, 33, 4400, …, 1/2, 1/4), although they vary in scope.

Such numerals usually refer to some ‘objects’ (e.g., three eggs, 5 roses, 33 potatoes, 4400 inhabitants, … 1/2 pound of flour, 44 liters of rainfall in an hour, …) located in a specific area.

A comprehensible verification then depends on the following factors:

• Can the specified number or quantity be directly determined in this area (a clear number must come out)?
• If the number or amount is too large to estimate directly in the area, is there a comprehensible procedure by which this is possible?
• What is the time required to make the determination in the area (e.g., minutes, hours, days, weeks, …)? If the necessary time always increases, it becomes increasingly difficult to make the statement for a specific time (e.g., the number of residents in a city).

These examples show that the question of verification quickly encompasses more and more aspects that must be met for the verifiability of a statement to be understood and accepted by all involved.

Language with Abstractions

Another pervasive feature of everyday languages is the phenomenon that, in the context of perception and memory (storing and recalling), abstract structures automatically form, which are also reflected in the language. Here are some simple examples:

IMAGE: Four types of objects, each seen as concrete examples of an abstract type (class).

In everyday life, we have a word for the perceived objects of types 1-4, even though the concrete variety makes each object look different: In the case of objects of group 1, we can speak of a ‘clock,’ for group 2 of a ‘cup,’ for 3 of ‘pens,’ and in the case 4 of ‘computer mice,’ or simply ‘mice,’ where everyone knows from the context that ‘mouse’ here does not mean a biological mouse but a technical device related to computers. Although we ‘sensorily’ see something ‘different’ each time, we use the ‘same word.’ The ‘one word’ then stands for potentially ‘many concrete objects,’ with the peculiarity that we ‘implicitly know’ which concrete object is to be linked with which word. If we were not able to name many different concrete objects with ‘one word,’ we would not only be unable to invent as many different words as we would need, but coordination among ourselves would completely break down: how could two different people agree on what they ‘perceive in the same way’ if every detail of perception counted? The same object can look very different depending on the angle and lighting.

The secret of this assignment of one word to many sensually different objects lies not in the assignment of words to elements of knowledge, but rather the secret lies one level deeper, where the events of perception are transformed into events of memory. Simplifying, one can say that the multitude of sensory events (visual, auditory, gustatory (taste), tactile, …) after their conversion into chemical-physical states of nerve cells become parts of neuronal signal flows, which undergo multiple ‘processings’. As a result, the ‘diversity of signals’ is condensed into ‘abstract structures’ that function as a kind of ‘prototype’ connected to many concrete ‘variants.’ There are thus something like ‘core properties’ that are ‘common’ to different perception events like ‘cup,’ and then many ‘secondary properties’ that can also occur, but not always, the core properties do. In the case of the ‘clock,’ for example, the two hands along with the circular arrangement of marks could be such ‘core properties.’ Everything else can vary greatly. Moreover, the ‘patterns of core and secondary properties’ are not formed once, but as part of processes with diverse aspects e.g., possible changes, possible simultaneous events, etc., which can function as ‘contexts’ (e.g., the difference between ‘technical’ and ‘biological’ in the case of the term ‘mouse’).

Thus, the use of a word like ‘clock’ or ‘cup’ involves— as previously discussed—once the reference to memory contents, to perceptual contents, to learned meaning relationships, as well as the ability to ‘decide’ which of the concrete perception patterns belong to which learned ‘prototype.’ Depending on how this decision turns out, we then say ‘clock’ or ‘cup’ or something else accordingly. This ability of our brain to ‘abstract,’ by automatically generating prototypical ‘patterns’ that can exemplify many sensorially different individual objects, is fundamental for our thinking and speaking in everyday life. Only because of this ability to abstract can our language work.

It is no less impressive that this basic ‘ability to abstract’ of our brain is not limited to the relationship between the two levels ‘sensory perception’ and ‘storage in memory,’ but works everywhere in memory between any levels. Thus, we have no problem grouping various individual clocks based on properties into ‘wristwatches’ and ‘wall clocks.’ We know that cups can be seen as part of ‘drinking vessels’ or as part of ‘kitchenware.’ Pens are classified as ‘writing instruments,’ and ‘computer mice’ are part of ‘computer accessories,’ etc.

Often, such abstraction achievements are also referred to as ‘categorizations’ or ‘class formation,’ and the objects that are assigned to such class designations then form the ‘class content,’ where the ‘scope’ of a class is ‘fluid.’ New objects can constantly appear that the brain assigns to one class or another.

Given this diversity of ‘abstractions,’ it is not surprising that the assignment of individual objects to one of these classes is ‘fluid,’ ‘fuzzy.’ With the hundreds or more different shapes of chairs or tables that now exist, it is sometimes difficult to decide, is this still a ‘chair’ or a ‘table’ in the ‘original sense’ [2] or rather a ‘design product’ in search of a new form.

For the guiding question of the verifiability of linguistic expressions that contain abstractions (and these are almost all), it follows from the preceding considerations that the ‘meaning of a word’ or then also the ‘meaning of a linguistic expression’ can never be determined by the words alone, but almost always only by the ‘context’ in which the linguistic expression takes place. Just as the examples with the ‘numerical words’ suggest, so must one know in a request like “Can you pass me my cup” which of the various cups was the ‘speaker’s cup.’ This presupposes the situation and ‘knowledge of the past of this situation’: which of the possible objects had he used as his cup?[3]

Or, when people try to describe a street, a neighborhood, a single house, and the like with language. Based on the general structures of meaning, each reader can form a ‘reasonably clear picture’ ‘in his head’ while reading, but almost all details that were not explicitly described (which is normally almost impossible) are then also not present in the reconstructed ‘picture in the head’ of the reader. Based on the ‘experience knowledge’ of the language participants, of course, everyone can additionally ‘color in’ his ‘picture in the head.'[4]

If a group of people wants to be sure that a description is ‘sufficiently clear,’ one must provide additional information for all important elements of the report that are ‘ambiguous.’ One can, for example, jointly inspect, investigate the described objects and/ or create additional special descriptions, possibly supplemented by pictures, sound recordings, or other hints.

When it comes to details, everyday language alone is not enough. Additional special measures are required.[5]

[1] A problem that machine image recognition has struggled with from the beginning and continues to struggle with to this day.

[2] The ‘original’ sense, i.e., the principle underlying the abstraction performance, is to be found in those neuronal mechanisms responsible for this prototype formation. The ‘inner logic’ of these neuronal processes has not yet been fully researched, but their ‘effect’ can be observed and analyzed. Psychology has been trying to approximate this behavior with many model formations since the 1960s, with considerable success.

[3] Algorithms of generative artificial intelligence (like chatGPT), which have no real context and which have no ‘body-based knowledge,’ attempt to solve the problem by analyzing extremely large amounts of words by breaking down documents into their word components along with possible contexts of each word so that they can deduce possible ‘formal contexts,’ which then function as ‘quasi-meaning contexts.’ To a certain extent, this works meanwhile quite well, but only in a closed word space (closed world).

[4] A well-known example from everyday life here is the difference that can arise when someone reads a novel, forms ideas in their head, and eventually someone produced a movie about the novel: to what extent do the ideas one has made of individual people correspond with those in the movie?

[5] Some may still know texts from so-called ‘holy scriptures’ of a religion (e.g., the ‘Bible’). The fundamental problem of the ‘ambiguity’ of language is of course intensified in the case of historical texts. With the passage of time, the knowledge of the everyday world in which a text was created is lost. Then, with older texts, there is often a language problem: the original texts, such as those of the Bible, were written in an old Hebrew (‘Old Testament’) or an old Greek (‘New Testament’), whose language use is often no longer known. In addition, these texts were written in different text forms, in the case of the Old Testament also at different times, whereby the text has also been repeatedly revised (which is often also connected with the fact that it is not clear who exactly the authors were). Under these conditions, deducing an ‘exact’ meaning is more or less restricted or impossible. This may explain why interpretations in the approximately 2000 years of ‘Bible interpretation’ have been very different at all times.

Author: Gerd Doeben-Henisch

Time: Jan 8, 2024 – Jan 8, 2024 (10:00 a.m. CET)

Email: gerd@doeben-henisch.de

TRANSLATION: The following text is a translation from a German version (?) into English. For the translation I am using the software deepL.com as well as chatGPT 4.

(?) The German source isn’t any more existent. Reason unknown.

Author: Gerd Doeben-Henisch

Time: Nov 12, 2023 — Nov 12, 2023

Email: gerd@doeben-henisch.de

–!! This is not yet finished !!–

CONTEXT

This text belongs to the topic Philosophy (of Science).

INTRODUCTION

The ‘coming out’ of a new type of ‘text generator’ in November 2022 called ‘chatGPT’ — this is not the only one around — caused an explosion of publications and usages around the world. The author is working since about 40 years in this field and never did this happen before. What has happened? Is this the beginning of the end of humans being the main actors on this planet (yes, we know, not really the best until now) or is there something around and in-between which we do overlook captivated by these new text generators?

Reading many papers since that event, talking with people, experimenting directly with chatGPT4, continuing working with theories and working with people in a city by trying new forms of ‘citizens at work in their community’, slowly a picture was forming in my head how it could perhaps be possible to ‘benchmark’ text generators with human activities directly.

After several first trials everything came together when I could give a speech in the Goethe University in Frankfurt Friday Nov-10. [1] There was a wonderful audience with elderly people from the so-called University of the 3rd age … a bit different to young students 🙂

There was an idea that hit me like a bolt of lightning when I wrote it down afterwards: it is the fundamental role of literature for our understanding of world and people, which will be completely eliminated by using text generators. The number of written text will explode in the near future, but the meaning of the world will vanish more and more at the same time. You will see letters, but there will be no more meaning around. And with the meaning the world of humans will disappear. You won’t even be able to know yourself anymore.

Clearly, it can only happen if we substitute our own thinking and writing completely by text generators.

Is the author of this text a bit ‘ill’ to write down such ideas or are there some arguments around which make it clear why this can be the fate of humans after the year 2023?

SET-UP OF AN EXPERIMENT

…

[1] See the text written down after the speech: https://www.cognitiveagent.org/2023/11/06/kollektive-mensch-maschine-intelligenz-im-kontext-nach