Trying to track down this study is not yielding much. I do know it was based on control systems theory in the era when “cybernetics” was a thing. After giving GPT5.2 some background, I asked:
I’m interested in the macrosocioeconomic analogues to electrical circuit quantities that might have been used with extensions of SPICE to Cray supercomputers of the era.
Got it — you’re asking for the actual quantity-level analogies that would have made sense to EE/control people in the SPICE + Cray era, not a modern ABM gloss. I’ll stay in their language.
Below is a clean mapping between classical circuit quantities and macrosocioeconomic quantities, framed the way a 1970s–1980s systems engineer would have thought about it.
1. The governing assumption (era-correct)
The working assumption was:
Macrosocial systems can be represented as lumped-parameter, nonlinear, dissipative dynamical systems with delayed feedback and poorly characterized components.
Which is exactly how large analog circuits were treated before VLSI abstraction took over.
2. Canonical quantity mapping
Effort / Flow variables (the backbone)
| Electrical |
Meaning |
Macrosocioeconomic analogue |
Interpretation |
| Voltage VVV |
Potential / pressure |
Incentive gradient |
Wage differential, price differential, regulatory pressure |
| Current III |
Flow |
Transaction / activity rate |
Money flow, migration rate, hiring rate, throughput |
| Power P=VIP = VIP=VI |
Energy transfer rate |
Economic activity rate |
GDP flow, resource burn, institutional load |
| Charge QQQ |
Accumulated potential |
Stored obligation / entitlement |
Debt, promises, backlog, social claims |
This mapping is not metaphorical. It is operational.
3. Storage elements (state)
Capacitors → Social stock variables
| Electrical capacitor |
Social analogue |
Stored quantity |
| Capacitance CCC |
Institutional buffering capacity |
Ability to absorb shocks |
| Charge QQQ |
Accumulated unmet demand |
Housing waitlists, court backlog |
| Voltage across C |
Social stress level |
Rent stress, congestion pressure |
Equation:
Q˙=Iin−Iout\dot{Q} = I_{\text{in}} - I_{\text{out}}Q˙=Iin−Iout
Used directly for:
- welfare rolls
- unemployment pools
- hospital queues
- infrastructure maintenance debt
Inductors → Inertia / momentum
| Electrical inductor |
Social analogue |
Meaning |
| Inductance LLL |
Institutional inertia |
Resistance to rapid change |
| V=LI˙V = L \dot{I}V=LI˙ |
Force needed to change flow |
Cost of accelerating reform |
Used for:
- labor market adjustment
- capital reallocation
- organizational retraining
- demographic momentum
This is where path dependence lives.
4. Dissipation and friction
Resistors → Transactional loss
| Electrical resistor |
Social analogue |
Examples |
| Resistance RRR |
Friction / overhead |
Compliance cost, red tape |
| V=IRV = IRV=IR |
Loss per unit flow |
Tax wedge, admin burden |
| Power dissipation |
Waste / inefficiency |
Deadweight loss |
This was how:
- bureaucracy
- corruption
- legal overhead
were naturally represented.
5. Nonlinear elements (where things get interesting)
Diodes → Threshold effects
| Electrical diode |
Social analogue |
| Forward bias |
Eligibility threshold |
| Reverse bias |
Exclusion / enforcement |
| Breakdown |
Institutional failure |
Used for:
- welfare eligibility cliffs
- credit access
- policing thresholds
- regulatory enforcement
Saturation & hysteresis
| Electrical effect |
Social analogue |
| Op-amp saturation |
Capacity exhaustion |
| Magnetic hysteresis |
Trust loss / recovery asymmetry |
| Deadband |
Political inaction zone |
These were known killers of stability in both domains.
6. Delays (explicitly modeled)
SPICE extensions on Cray explicitly modeled delays because they destroy stability.
| Electrical delay |
Social analogue |
| Transport delay |
Policy implementation lag |
| Sensor delay |
Data/reporting lag |
| Actuator delay |
Hiring/building/training time |
Delay + gain ⇒ oscillation
This was considered almost a law.
7. Feedback loops (policy = controller)
Negative feedback (stabilizers)
| Circuit |
Social system |
| Regulator |
Automatic stabilizers |
| AGC loop |
Central bank reaction |
| Thermal control |
Public health response |
Positive feedback (runaway)
| Circuit |
Social pathology |
| Oscillator |
Boom–bust cycles |
| Latch |
Institutional lock-in |
| Thermal runaway |
Inflation spirals |
Engineers were much more comfortable calling these design failures, not mysteries.
8. Noise sources
| Electrical noise |
Social analogue |
| Thermal noise |
Individual variability |
| Shot noise |
Discrete decision effects |
| Flicker noise |
Cultural drift |
Noise was treated as state-dependent, not iid — unlike statistical sociology.
9. What SPICE-on-Cray was actually used for
Cray-class machines enabled:
- very large coupled ODE systems
- stiff solvers (due to delays + nonlinearities)
- Monte Carlo parameter sweeps
- stability margin analysis
- bifurcation detection
In social terms:
- “How close is this policy to oscillation?”
- “Which parameter actually controls stability?”
- “What happens if this delay doubles?”
These are structural questions, not statistical ones.
10. Why sociology never adopted this
From an EE/control POV, the reason is blunt:
Statistics answers estimation questions.
Circuits answer design questions.
Policy is a design problem.
But:
- design assigns responsibility
- instability implies error
- feedback implies control
Those were (and are) politically radioactive.
11. The quiet irony
Modern ML systems:
- rediscover state
- rediscover feedback
- rediscover nonlinearity
- rediscover simulation
…but without the explicit conservation laws and stability theory that circuit analogies enforced.
So the old approach was, in some ways, more disciplined than today’s.