Computational model

Types of computational models

Below is a structured list of computational models. The spectrum is organized from fully deterministic and centralized at one end, to decentralized, adaptive, and emergent systems at the other.

#	Model Type	Name	Key Properties	Position
1	Deterministic	Finite State Machine (FSM)	Static states, input-driven transitions, no memory beyond state	Minimal autonomy, centralized
2	Deterministic	Pushdown Automaton (PDA)	FSM + stack (memory), suitable for parsing nested structures	FSM with memory
3	Deterministic	Turing Machine	Infinite tape memory, theoretical basis of general computation	Centralized but powerful
4	Reactive Systems	Event-Driven Architectures (EDA)	Stateless or stateful, reacts to events, e.g., microcontrollers	Still centralized or module-local
5	Distributed State Machines	State Machine Replication (SMR)	FSMs replicated across nodes (e.g., Raft, Paxos), ensuring consistency	Blockchain consensus layer fits here
6	Distributed Execution	Smart Contracts (on Blockchain)	FSM-like, deterministic, distributed, passive, consensus-driven	Bridges centralized logic and distributed deployment
7	Logic-based	Rule-Based Systems / Expert Systems	IF-THEN rules, possibly with a working memory	Central but symbolic reasoning
8	Knowledge-based	Production Systems	Rule sets with inference engine and memory (e.g., OPS5, CLIPS)	Local reasoning; semi-autonomous
9	Object-Oriented	Actors Model	Concurrent entities with private state, communicate via async messages	Distributed, loosely coupled
10	Declarative/Reactive Constraint Satisfaction Systems	Solves problems by propagating constraints across variables	Reactive, can be distributed
11	Autonomous Agents	BDI Agents (Belief-Desire-Intention)	Agents with internal goals, beliefs, and planning	Autonomous, goal-directed
12	Adaptive	Reinforcement Learning Agents	Learn behavior through environment interaction and reward	Adaptive, often centralized
13	Distributed Autonomous	Swarm Intelligence / Stigmergic Systems	Indirect coordination via environment (stigmergy), e.g., ant colony	Bottom-up emergence, no central control
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
16	Decentralized Social Machines	Holonic Systems / Digital Ecosystems	Self-similar agents (holons), recursive coordination, adaptability	Edge of MAS and beyond

Key Axes of Differentiation

Axis	Examples of Shift
Statefulness	FSM → PDA → Turing Machine → Smart Contract
Execution Environment	Centralized → Distributed (e.g. Smart Contracts, Actor Model)
Adaptivity	Rule-Based → BDI → RL → MAS
Agency / Autonomy	FSM (none) → Actors → BDI → MAS
Coordination Direct	call → Message passing → Stigmergy → Emergence
Emergent Behavior	Absent in FSM, present in Swarm Systems, MAS
Learning Capability	Only appears in RL agents and above

Summary Diagram (Linear Spectrum Approximation)

FSM ─ PDA ─ Turing ─ EDA ─ SMR ─ Smart Contracts ─ Actor Model ─ BDI ─ RL Agents ─ Swarm ─ MAS

--^ ^-- |--------------------------------------------------------------------------------------------|

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Deterministic, Centralized Distributed, Adaptive, Emergent

Relationship to Smart Contracts

Smart contracts could be:

• Implemented as FSMs (e.g., token contracts: Minted → Active → Burned).
• Hosted within replicated state machines (e.g., Ethereum Virtual Machine).
• Used by multi-agent systems as coordination primitives, rules enforcement, registries, or economic boundary conditions.

They lack:

• Adaptation (no learning),
• Proactivity (they don’t initiate),
• Perception (they don’t sense beyond what is input),
• Multi-agent awareness (no negotiation or emergent coordination).

Smart contracts are more advanced than FSMs due to their persistence, distributed execution, and ability to encode rich transactional logic, but they lack the autonomy, learning, and dynamic coordination mechanisms of multi-agent systems.

Smart contracts are like distributed finite state machines embedded in a shared, tamper-resistant ledger. They serve as deterministic, passive components in a distributed system, closer to FSMs, but operationally deployed in a distributed context like MAS.

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.