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

1. Modeling Technology Transitions under Increasing Returns, Uncertainty, and Heterogeneous Agents Tieju Ma Transition to New Technology (TNT) International Institute for Applied Systems Analysis

2. 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.

3. Technological learning (Increasing return) Reductions of investment costs for three representative new and advanced technologies Source: Nebojsa Nakicenovic, Technological change and diffusion as a learning process

4. 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);

5. 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.

6. 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.

7. 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

8. 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.

9. Uncertain learning Uncertain carbon tax Simulations with one agent Deterministic learning

10. Historical technology substitution patterns Competition among multiple technologies. The share of steel production in the United States by five different methods. From 1850 to ~ Source: Nakicenovic (1990); Grubler etc (1999)

11. 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)

12. Simulation with two agents and technology spillover Pioneer Follower

13. Diffusion pattern in real world Diffusion between leading and laggard markets Source: Grubler and Nakicenovic(1991)

14. Carbon abatement

15. 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

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