# Wright meets Markowitz: How standard portfolio theory changes when assets are technologies following experience curves

## TL;DR

This paper explores how standard portfolio theory adapts when assets are technologies that improve with experience, revealing complex optimization challenges and the impact of learning curves on diversification strategies.

## Contribution

It introduces a model integrating Wright's law into portfolio theory, analyzing the effects of learning curves on optimal technology investment and diversification.

## Key findings

- Multiple local optima in portfolio choices due to feedback effects.
- The discount rate significantly influences optimal diversification.
- Nonlinear distortions of feasible portfolios caused by learning feedback.

## Abstract

We consider how to optimally allocate investments in a portfolio of competing technologies using the standard mean-variance framework of portfolio theory. We assume that technologies follow the empirically observed relationship known as Wright's law, also called a "learning curve" or "experience curve", which postulates that costs drop as cumulative production increases. This introduces a positive feedback between cost and investment that complicates the portfolio problem, leading to multiple local optima, and causing a trade-off between concentrating investments in one project to spur rapid progress vs. diversifying over many projects to hedge against failure. We study the two-technology case and characterize the optimal diversification in terms of progress rates, variability, initial costs, initial experience, risk aversion, discount rate and total demand. The efficient frontier framework is used to visualize technology portfolios and show how feedback results in nonlinear distortions of the feasible set. For the two-period case, in which learning and uncertainty interact with discounting, we compare different scenarios and find that the discount rate plays a critical role.