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
->-------------------------------------------------------------------------------------------------------------------------------------<-
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.