ChatGPT describes the model of a participatory economy

1

ChatGPT is an artificial intelligence chatbot developed by the Microsoft-aligned company OpenAI and made public in November 2022. You type in questions and ChatGPT, in a matter of seconds, delivers answers.

I asked ChatGPT a number of questions and requests related to the model of a participatory economy. Here’s the first of those questions, followed by my reply.

Give me a brief summary of Robin Hahnel’s model of a participatory economy.

Robin Hahnel is an American economist who developed the model of a participatory economy as an alternative to traditional capitalist and centrally planned socialist systems. His model, known as “participatory economics” or “parecon” for short, aims to address issues of economic inequality and promote democratic decision-making within the economic sphere.

In a participatory economy, the means of production are collectively owned by the workers and the community as a whole. The economy is organized around self-managed worker councils and consumer councils, which operate through a process of participatory planning.

Worker councils are responsible for decision-making within workplaces, including production decisions, work schedules, and remuneration. Each worker has an equal say in the decision-making process, ensuring that power is distributed democratically. These councils also coordinate with other councils to address issues of resource allocation and production coordination.

Consumer councils, on the other hand, represent the interests of consumers and play a role in determining the distribution of goods and services. They express their preferences and needs through a participatory planning process, which involves proposing and ranking consumption requests.

The participatory planning process is designed to balance efficiency and equity. It involves iterative rounds of proposals, revisions, and negotiations between worker and consumer councils until a consensus is reached. The aim is to allocate resources and determine production levels based on democratic deliberation and social priorities rather than market forces or central planning directives.

To address issues of income inequality, parecon advocates for equitable remuneration based on effort and sacrifice. Hahnel suggests a system where people are compensated for their work based on the amount of effort expended and the intensity of the work, as well as the duration and onerousness of the labor performed.

Hahnel’s participatory economy model seeks to establish economic structures that are compatible with democratic principles, emphasizing decision-making power, equity, and the meeting of human needs. However, it should be noted that this is a brief summary of Hahnel’s model, and there are more detailed aspects and nuances to consider when exploring the full scope of participatory economics.

My take on ChatGPT’s response: It’s an acceptable brief summary of the model, heavily based on previous descriptions and summaries of the model. I wish ChatGPT elaborated a bit more on the details and also mentioned one or more examples, but it’s sufficient boilerplate.

2

Rather good.

But it is an example of SHIT work. Seemingly Humanlike Intelligent Tech. S.H.I.T. So heading into the future it seems we will all just have to put up with more and more SHIT. We’ll never be free of SHIT. Shit.

3

Here we are a couple of years later. AI is likely more advanced than it was in 2023. So I asked the the following question…

How could artificial intelligence be leveraged in the administration of the participatory economy?

Here is the response…

Excellent question. A participatory economy (Parecon) relies on iterative planning, council deliberation, and allocation of resources without markets or central command. That sounds complex — but artificial intelligence (AI) could make it much more practical and efficient.

Here’s how AI could be leveraged:

⸻

  1. Data Processing & Optimization
    • Parecon requires balancing millions of consumption and production proposals.
    • AI can rapidly analyze these inputs, simulate trade-offs, and generate indicative prices that reflect labor, ecological, and resource costs.
    • Instead of long rounds of manual negotiation, AI could shorten planning cycles by crunching numbers in hours rather than weeks.

⸻

  1. Resource Allocation & Forecasting
    • AI can forecast future demand and supply patterns (e.g., food needs, healthcare services, energy use).
    • Helps councils anticipate bottlenecks and propose realistic production plans.
    • Could incorporate ecological models (carbon emissions, resource depletion) into indicative prices automatically.

⸻

  1. Participatory Platforms
    • AI-powered platforms could allow every worker and consumer to input preferences easily (apps, voice assistants, kiosks).
    • AI would summarize proposals, highlight conflicts, and suggest compromise options for councils.
    • Natural language processing could make complex planning data accessible in plain language.

⸻

  1. Effort and Sacrifice Assessment
    • In Parecon, rewards are based on effort and sacrifice, which are subjective and prone to disputes.
    • AI could help councils track working hours, task difficulty, stress levels (self-reported + objective factors like night shifts or hazardous conditions).
    • This doesn’t eliminate human judgment, but provides a transparent baseline to guide fair credit allocation.

⸻

  1. Innovation & Research Allocation
    • AI could identify areas where R&D investment is most socially valuable (renewable energy, medicine, urban design).
    • It can simulate different innovation scenarios and help councils decide which projects merit collective resources.

⸻

  1. Public Works and Infrastructure
    • For projects like water systems, transport, or energy grids, AI could optimize design, maintenance schedules, and resource allocation.
    • Councils would still make decisions, but AI would provide the technical blueprint and ecological cost analysis.

⸻

  1. Transparency & Accountability
    • AI can track resource flows in real-time, ensuring councils and the public see where every resource is going.
    • This reduces opportunities for corruption, inefficiency, or bureaucratic gridlock.
    • Citizens could query the system: “Why is steel production limited this year?” — and get an intelligible answer.

⸻

  1. Education & Participation Support
    • Not everyone has the same economic literacy. AI could act as a personal assistant helping citizens understand options, trade-offs, and how their input affects the plan.
    • Example: “If your council approves this project, electricity prices may rise 2%. Do you want to proceed?”

⸻

Risks & Safeguards
• Bias & control: AI must be open-source and overseen democratically, or it risks creating a new “coordinator class” of tech experts.
• Over-automation: AI should assist, not replace, human deliberation — final decisions stay with councils.
• Privacy: Systems must be transparent without intruding on personal data beyond what’s necessary for planning.

⸻

:white_check_mark: Key Takeaway

AI could be the nervous system of a participatory economy — processing massive amounts of data, forecasting needs, and presenting options to councils — while humans remain the decision-makers. This would make Parecon far more scalable, efficient, and adaptable than in the original 1990s proposals, where computation was a bottleneck.

⸻

Would you like me to sketch a future scenario (say, the year 2045) showing how a participatory economy with AI actually runs day-to-day — like a narrative of how citizens and councils interact with the AI system?

Perfect — let’s do a narrative scenario. I’ll sketch a day-in-the-life in the year 2045, where a participatory economy is running with AI as its supportive infrastructure.

⸻

:globe_showing_europe_africa: Life in a Participatory Economy, 2045

Morning – Personal Planning

Maria wakes up in her apartment in Montréal. Before heading to her shift at the community health center, she checks her AI assistant, a publicly owned system connected to the participatory economy network.
• The assistant updates her on her effort credits:
• She earned 152 credits last month, reflecting her mix of day shifts, two night shifts, and one week in the emergency ward.
• Credits are automatically logged by her workplace council and verified by the AI system.
• Maria asks:
“Why are fresh berries more expensive this month?”

•	The AI explains: “Climate shifts reduced yields in Chile; importing more would raise carbon costs. Regional councils voted to prioritize greenhouse expansion instead. Price increase is temporary.”

Maria accepts the trade-off. She chooses to buy more apples instead, which the assistant helps her adjust in her weekly grocery request.

⸻

Midday – At Work

At the health center, Maria joins a quick council meeting. The AI system projects data:
• Patient loads, staffing levels, and resource usage.
• A simulation showing how reallocating two nurses to the home-care program would reduce ER wait times by 18%.

The council discusses, weighs the social benefits, and votes. AI provides scenarios and consequences, but decisions remain democratic.

⸻

Afternoon – Collective Projects

Maria logs into the city-wide council platform. A major decision is up for review: building a new wastewater treatment plant.
• The AI presents three options:
1. Conventional expansion (cheaper, moderate ecological impact).
2. Green technology pilot (higher resource use now, but long-term ecological benefit).
3. Hybrid upgrade (balanced costs and ecological outcomes).

Each option shows:
• Materials needed.
• Labor hours required.
• Ecological impact (carbon, water use).
• Effects on future indicative prices (how much people would “pay” in credits for water services).

Neighborhood councils vote after discussion, and Maria’s local council chooses the hybrid upgrade.

⸻

Evening – Education and Leisure

Later, Maria’s daughter uses the AI system to pick classes for her next semester. The assistant helps balance her interests (biology, music) with societal needs (more healthcare workers, renewable engineers). It doesn’t force her choices — but shows how each path contributes to society’s collective plan.

At night, Maria attends a cultural event. She spends a few credits for tickets, but basic access to libraries, community centers, and digital platforms is free as a public good, since they’re funded collectively through the planning system.

⸻

End of Year – Planning Cycle

In December, Maria and her family submit their consumption proposals for the next year. The AI assistant helps them balance desires with social and ecological costs, explaining trade-offs in plain language.
• Maria’s family requests:
• More fresh produce.
• Participation in a community vacation housing program.
• A new e-bike for her daughter.

The AI notes the ecological costs of the e-bike (rare metals) and suggests a refurbished model. The family accepts.

Their council aggregates requests, AI runs the data, and higher federations reconcile all inputs. After a few planning rounds, the indicative prices settle and production is adjusted accordingly.

⸻

:white_check_mark: Key Features in This Future
• AI as transparent coordinator: crunching huge datasets, simulating outcomes, making the system efficient.
• Humans as decision-makers: councils debate and vote; AI provides analysis, not commands.
• Effort-based remuneration: Maria’s credits come from work hours, conditions, and sacrifice, not output or property.
• Public goods free at use: healthcare, education, infrastructure are funded collectively.
• Ecological costs visible: AI integrates environmental accounting into every choice.
• Democratic accessibility: citizens can query the system in everyday language and get clear answers.

⸻

:seedling: So daily life feels different from capitalism: no taxes, no bosses, no private accumulation — but still structured, efficient, and innovative, because AI makes complex participatory planning manageable at scale.