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 Differentiatio
| 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 |