Modeling Technology Transitions under Increasing Returns, Uncertainty, and Heterogeneous Agents

Tieju Ma

Transition to New Technology (TNT)International Institute for Applied Systems Analysis

Three missing “stylized facts” in traditional technological change models

Increasing returns to adoption (Endogenous technological learning)

Uncertainty

Heterogeneous agents following diverse technology development and adoption strategies.

Technological learning (Increasing return)

Reductions of investment costs for three representative new and advanced technologiesSource: Nebojsa Nakicenovic, Technological change and diffusion as a learning process

Uncertainty

Range of Future Investment Cost Distributions from the IIASA Technology Inventory for Biomass, Nuclear, and Solar Electricity-Generation Technologies, in US(1990)$ per kilowatt (KW).Sources: Messener and Strubegger (1991); Nakicenovic et al. (1998);

Heterogeneous agents (actors)

Traditional model assume a “global social planner”;

In reality, there are different actors with heterogeneous attributes, e.g. different attitude to risk.

Purpose

Model endogenous technology transitions under the three important "stylized facts" governing technological change.

The main objective of the model is for exploratory modeling purposes and as a heuristic research device to examine in depth the impacts of alternative model formulations on the endogenous technology transition dynamics.

A highly stylized model-- Inspired by energy and climate change policy models

One primary resource, whose extraction costs increase over time as a function of resource depletion.

One homogeneous good, the demand for which increases over time.

Three technologies: Existing -- entirely mature, constant cost and efficiency,

high emission Incremental -- slight efficiency advantage, higher initial cost

(2), potential for technological learning (10%), low emission Revolutionary -- requires no resource input, much higher

initial cost (40) , higher learning potential (30%), no emission

Optimization model

Uncertainty in the model

Uncertain learning rate: the learning rates are treated as random values characterized by a distribution function.

Uncertain carbon tax. The existence, magnitude and the timing of introducing carbon tax are treated as uncertain, characterized by different distribution functions.

We generate N sample of random variables, and then the average cost resulted from overestimating or underestimating the variables is added into objective function.

Solutions are optimal hedging strategies against risk.

Simulations with one agent

Deterministic learning

Uncertain learning

Uncertain carbon tax

Historical technology substitution patterns

Source: Nakicenovic (1990); Grubler etc (1999)

Competition among multiple technologies.The share of steel production in the United States by five different methods.From 1850 to ~

Pareto optimization with two heterogeneous agents

Different risk attitude and different weights Trading on good Trading on resource Technology spillover

Pareto Optimality:

The "best that could be achieved without disadvantaging at least one group." (Allan Schick, in Louis C. Gawthrop, l970, p.32)

Simulation with two agents and technology spillover

Pioneer

Follower

Diffusion pattern in real world

Diffusion between leading and laggard markets

Source: Grubler and Nakicenovic (1991)

Carbon abatement

Concluding remarks

The highly stylized model and simulations can enhance people’s imagination about how the three stylized facts impact technological change processes.

In addition, the simulation results can give some policy implications for both risk-taking and risk-aversion decision makers, e.g., for risk-aversion agent, it is better to import a new technology from risk-taking agent at the niche market stage of the new technology, instead of waiting until the new technology being mature.

History (story) -based VS Equation-based

Thanks for your attention!