Computational model - Revision history

TiberiusB: /* Types of computational models */

2025-10-24T19:54:26Z

Types of computational models

TiberiusB: /* Types of computational models */

2025-10-24T19:52:27Z

Types of computational models

TiberiusB: /* Types of computational models */

2025-10-24T19:51:03Z

Types of computational models

TiberiusB: /* Types of computational models */

2025-10-24T19:50:40Z

Types of computational models

TiberiusB: /* Types of computational models */

2025-10-24T19:45:49Z

Types of computational models

TiberiusB: /* Conclusion: fundamental incompatibility */

2025-07-26T00:26:12Z

Conclusion: fundamental incompatibility

TiberiusB: /* XXX */

2025-07-25T23:40:07Z

XXX

TiberiusB: /* Relationship to Smart Contracts */

2025-07-25T23:38:20Z

Relationship to Smart Contracts

TiberiusB: /* Types of computational models */

2025-07-25T23:34:56Z

Types of computational models

TiberiusB: /* Types of computational models */

2025-07-25T23:33:42Z

Types of computational models

@@ Line 66: / Line 66: @@
 In modern implementations (e.g., decentralized holonic control in manufacturing or IoT), agents negotiate and reorganize dynamically, producing multiple possible outcomes from identical starting configurations.
-Cognitive Architectures
+From a theoretical standpoint:
-Architectures such as SOAR, ACT‑R, and Sigma employ fixed control cycles and production rules (deterministic bases), but their performance depends on activation levels, probabilistic retrievals, and environmental stimuli, making identical inputs sometimes yield different results.
+* Deterministic computation maps one input to one output.
+* Non‑deterministic computation explores multiple simultaneous paths or probabilistic transitions, aligning with adaptive, agentic, and emergent architectures.

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-Holonic and digital ecosystem systems are advancing rapidly across distributed AI, industrial IoT, and hybrid agentic frameworks. These architectures merge self-similar recursive structures (holons) with adaptive, cooperative intelligence at the edge of multi-agent systems (MAS).
+'''Holonic and digital ecosystem systems''' are advancing rapidly across distributed AI, industrial IoT, and hybrid agentic frameworks. These architectures merge self-similar recursive structures (holons) with adaptive, cooperative intelligence at the edge of multi-agent systems (MAS).
@@ Line 59: / Line 59: @@
 '''Holonic systems and digital ecosystems''' extend non‑determinism to structural and organizational levels:
 A holon’s state and behavior are locally autonomous yet recursively influenced by higher‑level holarchies, creating emergent global patterns not predictable from initial states alone.

← Older revision		Revision as of 19:51, 24 October 2025
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	In modern implementations (e.g., decentralized holonic control in manufacturing or IoT), agents negotiate and reorganize dynamically, producing multiple possible outcomes from identical starting configurations.		In modern implementations (e.g., decentralized holonic control in manufacturing or IoT), agents negotiate and reorganize dynamically, producing multiple possible outcomes from identical starting configurations.
		+
		+

	<big>Key Axes of Differentiation</big>		<big>Key Axes of Differentiation</big>

← Older revision		Revision as of 19:50, 24 October 2025
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	\|}		\|}

		+
		+	Holonic and digital ecosystem systems are advancing rapidly across distributed AI, industrial IoT, and hybrid agentic frameworks. These architectures merge self-similar recursive structures (holons) with adaptive, cooperative intelligence at the edge of multi-agent systems (MAS).
		+
		+
		+	both cognitive architectures (like SOAR and ACT‑R) and holonic/digital ecosystem systems can reasonably be classified as non‑deterministic computational models, although in subtly different ways.
		+
		+	'''Cognitive architectures''' typically combine deterministic rule‑based logic with stochastic learning or adaptive mechanisms, producing behavior that is non‑deterministic in outcome even if deterministic in structure.
		+
		+	Architectures such as SOAR, ACT‑R, and Sigma employ fixed control cycles and production rules (deterministic bases), but their performance depends on activation levels, probabilistic retrievals, and environmental stimuli, making identical inputs sometimes yield different results.
		+
		+	This is referred to as “soft determinism” or stochastic rule interaction, similar to probabilistic AI systems that adapt or explore within constraint spaces.
		+
		+	The result is bounded non‑determinism, where the architecture remains explainable but can flexibly handle uncertainty, incomplete data, or noise — essential for human‑like cognition.
		+
		+	'''Holonic systems and digital ecosystems''' extend non‑determinism to structural and organizational levels:
		+
		+	A holon’s state and behavior are locally autonomous yet recursively influenced by higher‑level holarchies, creating emergent global patterns not predictable from initial states alone.
		+
		+	Because these systems rely on distributed decision‑making, self‑organization, and recursive feedback, they behave as non‑deterministic complex adaptive systems — similar in spirit to non‑deterministic constraint logic models, where multiple valid computational paths coexist until resolution.
		+
		+	In modern implementations (e.g., decentralized holonic control in manufacturing or IoT), agents negotiate and reorganize dynamically, producing multiple possible outcomes from identical starting configurations.

	<big>Key Axes of Differentiation</big>		<big>Key Axes of Differentiation</big>

@@ Line 39: / Line 39: @@
 |14 || Distributed Coordination || Multi-Agent Systems (MAS) || Autonomous agents, possible learning, social protocols, communication || Fully distributed and adaptive
 |-
-|15 || Hybrid Adaptive || Cognitive Architectures (e.g., SOAR, ACT-R) || Integrates perception, planning, memory, learning || High autonomy, often hybrid centralized-distributed
+|15 || Hybrid Adaptive || Cognitive Architectures (e.g., SOAR, ACT-R, Brain-CA, CogTwin) || Integrates perception, planning, memory, learning || High autonomy, often hybrid centralized-distributed
 |-
-|16 || Decentralized Social Machines || Holonic Systems / Digital Ecosystems || Self-similar agents (holons), recursive coordination, adaptability || Edge of MAS and beyond
+|16 || Decentralized Social Machines || Holonic Systems / Digital Ecosystems (ex. HoL, Flexible Holons (MIT CCS), Adaptive Holonic Architectures (ACM 2008) || Self-similar agents (holons), recursive coordination, adaptability || Edge of MAS and beyond
 |}

← Older revision		Revision as of 00:26, 26 July 2025
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	* Computationally, FSMs lack the expressiveness to model context-sensitive human behaviors and face insurmountable state explosion problems. Practically, real-world implementations consistently fail when confronting the complexity and unpredictability of human societies. Philosophically, FSMs embody a reductionist, deterministic vision of governance that violates principles of human freedom, democratic participation, and dignity.		* Computationally, FSMs lack the expressiveness to model context-sensitive human behaviors and face insurmountable state explosion problems. Practically, real-world implementations consistently fail when confronting the complexity and unpredictability of human societies. Philosophically, FSMs embody a reductionist, deterministic vision of governance that violates principles of human freedom, democratic participation, and dignity.
	* While FSMs remain valuable for modeling simple, well-defined processes with clear state transitions, the attempt to use them for governing entire human communities represents a category error - trying to force the rich complexity of human social life into the impoverished framework of regular languages and finite states. The future of computational governance lies not in more sophisticated state machines but in adaptive, distributed systems that enhance rather than replace human agency and democratic deliberation.		* While FSMs remain valuable for modeling simple, well-defined processes with clear state transitions, the attempt to use them for governing entire human communities represents a category error - trying to force the rich complexity of human social life into the impoverished framework of regular languages and finite states. The future of computational governance lies not in more sophisticated state machines but in adaptive, distributed systems that enhance rather than replace human agency and democratic deliberation.
		+
		+	=See also=
		+	[[DAO]]

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 * Distributed, but not adaptive/multi-agent in the Holochain sense: Blockchain smart contracts are distributed in that their state and logic are replicated across many nodes for consensus and fault-tolerance, but each contract instance acts according to its predetermined logic and does not autonomously adapt or "learn" in interaction with others. In contrast, truly distributed, adaptive, multi-agent systems feature autonomous agents capable of negotiation, dynamic behavior adaptation, and collaborative problem-solving—none of which are inherent in typical smart contracts.
-=XXX=
+=The theoretical and practical limits of state machines for governing human communities=
 The theoretical and practical limits of state machines for governing human communities
 Based on extensive research across computer science, complexity theory, governance studies, and real-world implementations, finite state machines (FSMs) face insurmountable theoretical and practical limitations when applied to governing human communities at scale. These limitations stem from fundamental mathematical constraints, empirical evidence of systemic failures, and deep philosophical incompatibilities with human agency and democratic values.
 The computational impossibility of modeling human societies
 At the most fundamental level, FSMs occupy the bottom tier of the Chomsky hierarchy, capable only of recognizing regular languages. This severely limits their computational expressiveness compared to the requirements of modeling human social systems. Research from formal language theory conclusively demonstrates that FSMs cannot handle context-sensitive behaviors, recursive structures, or unbounded memory requirements - all essential features of human social interaction.
 The state explosion problem renders FSM-based governance mathematically impossible for realistic populations. To model even a small city of 100,000 people with minimal behavioral tracking would require approximately 10^200,000 states - a number so large it exceeds the total atoms in the observable universe by factors beyond comprehension. Memory requirements alone would make such systems impossible with any conceivable technology. Industrial verification tools struggle with systems containing more than 10^9 states, while meaningful social modeling would require exponentially more.
 This isn't merely a matter of needing more powerful computers. The computational complexity grows exponentially with population size, creating fundamental barriers that no amount of optimization can overcome. Even sophisticated mitigation strategies like hierarchical state machines or symbolic representations only reduce constants without addressing the underlying exponential growth.
 Evidence from real-world algorithmic governance failures
 Empirical evidence from attempts at algorithmic governance reveals consistent patterns of failure when rigid state-based approaches encounter the complexity of human societies. The Netherlands' System Risk Indication (SyRI) was shut down by courts in 2020 for violating human rights through opaque algorithmic discrimination. Despite years of operation, it failed to detect any new fraud cases while systematically targeting low-income neighborhoods.
 The UK's National Programme for IT in healthcare became one of history's most expensive IT failures, wasting over £12 billion attempting to model complex healthcare workflows as state machines. Similarly, the US Healthcare.gov launch in 2013 crashed immediately when real-world usage patterns exceeded the rigid assumptions built into its state-based architecture.
 Historical attempts at cybernetic governance provide particularly instructive failures. The Soviet Union's OGAS project, which aimed to manage the entire economy through networked computers and state machines, collapsed due to bureaucratic resistance and the fundamental inability to capture economic complexity in predetermined states. Chile's Project Cybersyn under Allende showed initial promise but revealed the limitations of trying to model dynamic social and economic systems through formal state representations.
 The emergence and adaptability challenge
 Human social systems exhibit emergent properties that fundamentally exceed the capabilities of finite state machines. Research from the Santa Fe Institute and complexity science demonstrates that social systems generate genuinely novel behaviors through non-linear dynamics and self-organization. These emergent phenomena cannot be captured by predetermined state transitions - they require computational models capable of generating new states and rules dynamically.
 FSMs fail catastrophically when encountering Black Swan events - statistically improbable occurrences with massive impact that lie outside the originally conceived state space. The 2008 financial crisis, Arab Spring, and COVID-19 pandemic all demonstrated how human societies face genuinely unprecedented situations requiring adaptive responses that no predetermined state machine could handle.
 Human creativity and innovation further expose FSM limitations. Innovation often involves breaking existing rules rather than following them - a capability fundamentally beyond state machines that can only execute predefined transitions. Successful human institutions exhibit "institutional learning" and evolve their own governing principles, requiring meta-governance capabilities that transcend fixed rule systems.
 Comparative analysis reveals superior alternatives
 Empirical comparisons between different computational approaches to social coordination consistently show the limitations of centralized, state-based systems. Multi-agent systems demonstrate O(N log N) computational complexity for modeling social interactions, compared to the exponential complexity of equivalent FSM representations. Agent-based models, being computationally universal (subject to the Church-Turing thesis), can capture emergent behaviors and adapt to new situations in ways FSMs cannot.
 Real-world comparative studies reinforce these theoretical findings. Wikipedia's hybrid human-algorithm governance model successfully scaled to millions of articles while maintaining quality, but its increasing automation correlates with declining editor retention and diversity. Blockchain governance experiments reveal that even with transparency advantages, rigid protocol-based systems struggle with adaptation and crisis response compared to traditional institutional flexibility.
 Philosophical and democratic incompatibilities
 Beyond technical limitations, FSMs embody a vision of governance fundamentally incompatible with human freedom and democratic values. Hannah Arendt's concept of "natality" - the human capacity for spontaneous, unpredictable action - stands in direct opposition to the deterministic nature of state machines. Politics requires accommodating genuine human action, not merely predictable behavior that follows predetermined patterns.
 The drive toward algorithmic legibility exemplifies what James C. Scott identified as "high modernist" failures - technocratic schemes that ignore local knowledge and human complexity. FSMs represent the ultimate expression of this impulse to make populations "legible" through standardized categories, but this reductionism systematically excludes the practical wisdom (metis) essential for effective governance.
 Democratic theorists like Cristina Lafont argue that algorithmic governance creates impermissible "shortcuts" around democratic deliberation. True democratic legitimacy requires that citizens see themselves as authors of the laws governing them - impossible when those laws are encoded in opaque state machines designed by technical elites. The "black box" nature of complex state systems makes democratic accountability and contestation impossible.
 The multi-agent alternative
 Research consistently points toward distributed, adaptive, multi-agent approaches as more suitable for social coordination. Unlike FSMs, multi-agent systems can:
-Handle emergent behaviors through agent interactions
+* Handle emergent behaviors through agent interactions
-Adapt to new situations without reprogramming
+* Adapt to new situations without reprogramming
-Scale efficiently with population size
+* Scale efficiently with population size
-Accommodate human agency and creativity
+* Accommodate human agency and creativity
-Integrate local knowledge and context
+* Integrate local knowledge and context
 Successful digital governance initiatives like Estonia's e-governance system and Taiwan's vTaiwan platform employ distributed architectures that preserve human agency while leveraging computational efficiency. These systems succeed precisely because they don't attempt to reduce human complexity to finite states but instead create frameworks for human-computer collaboration.
 Conclusion: fundamental incompatibility
 The evidence overwhelmingly demonstrates that finite state machines are not merely inadequate but fundamentally incompatible with governing human communities. This incompatibility manifests at multiple levels:
-Computationally, FSMs lack the expressiveness to model context-sensitive human behaviors and face insurmountable state explosion problems. Practically, real-world implementations consistently fail when confronting the complexity and unpredictability of human societies. Philosophically, FSMs embody a reductionist, deterministic vision of governance that violates principles of human freedom, democratic participation, and dignity.
+* Computationally, FSMs lack the expressiveness to model context-sensitive human behaviors and face insurmountable state explosion problems. Practically, real-world implementations consistently fail when confronting the complexity and unpredictability of human societies. Philosophically, FSMs embody a reductionist, deterministic vision of governance that violates principles of human freedom, democratic participation, and dignity.
-While FSMs remain valuable for modeling simple, well-defined processes with clear state transitions, the attempt to use them for governing entire human communities represents a category error - trying to force the rich complexity of human social life into the impoverished framework of regular languages and finite states. The future of computational governance lies not in more sophisticated state machines but in adaptive, distributed systems that enhance rather than replace human agency and democratic deliberation.
+* While FSMs remain valuable for modeling simple, well-defined processes with clear state transitions, the attempt to use them for governing entire human communities represents a category error - trying to force the rich complexity of human social life into the impoverished framework of regular languages and finite states. The future of computational governance lies not in more sophisticated state machines but in adaptive, distributed systems that enhance rather than replace human agency and democratic deliberation.

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 =Relationship to Smart Contracts=
 Smart contracts could be:
 * Implemented as FSMs (e.g., token contracts: Minted → Active → Burned).
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 They can serve as infrastructure for MAS, e.g., as rule enforcement layers, registries, or coordination contracts in larger multi-agent frameworks. But by themselves, smart contracts are not agents, nor are they adaptive.

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 ->-------------------------------------------------------------------------------------------------------------------------------------<-
-Deterministic, Centralized                                                                Distributed, Adaptive, Emergent
+Deterministic, Centralized------------------------------------------------Distributed, Adaptive, Emergent
 =Relationship to Smart Contracts=

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 FSM ─ PDA ─ Turing ─ EDA ─ SMR ─ Smart Contracts ─ Actor Model ─ BDI ─ RL Agents ─ Swarm ─ MAS
-->---------------------------------------------------------------------------------------------------------------------<-
+->-------------------------------------------------------------------------------------------------------------------------------------<-
 Deterministic, Centralized                                                                Distributed, Adaptive, Emergent