PREDICTION OF TECHNOLOGY EVOLUTION!

David Wolpert, dwolpert@lanl.gov

Strategic Latency Workshop, October, 2012

1)   A windmill

2)   An electrical infrastructure: windmills, PV farms, coal and oil power plants, the electrical grid, etc.

3)   The Iranian nuclear fuel complex

4)   A 3-d printer manufactured in South Korea

5)   A new approach to cyber-defense, designed in the US

Predicting Technology Evolution

Examples of technologies/

1)   A windmill

Predicting Technology Evolution

Examples of technologies/

Probability windmills will produce ≥ k watts / $ while producing ≤ K dB each in m years?

1)   A windmill

2)   An electrical infrastructure: windmills, PV farms, coal and oil power plants, the electrical grid, etc.

Predicting Technology Evolution

Examples of technologies/

Probability windmills will produce ≥ k watts / $ while producing ≤ K dB each in m years?

Probability infrastructure will produce ≤ x tons CO2 / watt while costing ≤ $z / watt in m years?

1)   A windmill

2)   An electrical infrastructure: windmills, PV farms, coal and oil power plants, the electrical grid, etc.

3)   A 3-d printer manufactured in South Korea

Predicting Technology Evolution

Examples of technologies/

Probability windmills will produce ≥ k watts / $ while producing ≤ K dB each in m years?

Probability infrastructure will produce ≤ x tons CO2 / watt while costing ≤ $z / watt in m years?

Probability S Korean 3-d printers will have ≥ y micron precision for ≤ $v with ≤ T month average failure intervals in m years?

1)   A windmill

2)   An electrical infrastructure: windmills, PV farms, coal and oil power plants, the electrical grid, etc.

3)   A 3-d printer manufactured in South Korea

4)   A new approach to cyber-defense, designed in the US

Predicting Technology Evolution

Examples of technologies/

Probability windmills will produce ≥ k watts / $ while producing ≤ K dB each in m years?

Probability infrastructure will produce ≤ x tons CO2 / watt while costing ≤ $z / watt in m years?

Probability S Korean 3-d printers will have ≥ y micron precision for ≤ $v with ≤ T month average failure intervals in m years?

Probability new approach ROC detection characteristics beat existing approach’s if new approach development funds = $w?

1)   Domain experts combine subjective expertise to predict evolution of a technology

2)   No quantitative modeling of stochastic process underlying that evolution

3)   Typically no exploitation of historical data (e.g., to fit parameters in a stochastic process model)

4)   Restricted by (implicit) biases of experts, and their inability to foresee scientific breakthroughs.

Trade Studies for Predicting Technology Evolution

Stationary Evolution of One-Dimensional Spec Vectors

Non-stationary Evolution of One-Dimensional Spec Vectors

Example of detecting non-stationarity

Observed data (a noisy laser):

Predictions for future based on observed data vs. actual future:

Example of detecting non-stationarity

Used a standard algorithm in time series analysis – which is far more sophisticated than Moore’s law.

Multi-dimensional spec vectors

1)   More accurate prediction evolution than with one-dimensional vectors.

•  No potentially relevant data thrown out

2)   Reveals potentially important correlations among likely characteristics of a technology

•  Probability ≥ .R that in m years stealth technology will have ≥ this radar reflectivity, or ≥ this infrared opacity

•  But ≤ .S probability it will have both

Multi-dimensional spec vectors

3) Tells us which characteristics of a technology are most important enablers of characteristics we care about

•  Potentially useful for choosing dual-use protocols •  Subtle statistical effects

4) Tells us which characteristics of a technology are most

predictive of its future evolution

•  Potentially useful for deciding how to focus intelligence assets

1)   A blueprint of a technology specifies its components and their interactions

2)   Blueprints are hierachical. E.g., blueprint of an IED includes blueprints of detonators which include blueprints of integrated circuits.

3)   A spec vector of a technology is its operational characteristics. E.g., the specs of an IED.

Blueprints and Spec Vectors

Technology evolution: A stochastic process in which multiple actors

search the space of all blueprints to find one with spec vector they desire.

Populations evolving in blueprint / spec space

Using Historical Data to Predict Technology Evolution

1)   Fit parameters of stochastic model of spec vector evolution to historical data

•  Use those parameters to provide probability distribution of future evolution of spec vectors

More ambitious – and potentially more powerful:

2)   Fit parameters of stochastic model of joint blueprint / spec vector evolution to historical data

•  Use those parameters to provide probability distribution of future evolution of spec vectors

CONCLUSION 1)   Ability to predict evolution of technology would allow us to:

i) Allocate our R&D resources better; ii) Better predict economic / environmental challenges; iii) Better predict comparative weakness of US industry iv) Better predict adversary military capabilities.

2)   Need probabilities over multi-dimensional spec spaces, generated from historical data together with domain experts

3)   Extensions of time-series analysis algorithms designed for this

4)   More ambitious still: Generate a probabilistic model of evolution in blueprint-spec space from historical data.