Post on 17-Aug-2015
Incremental and Breakthrough Innovation: An Agent-Based Model of
Firms and Technology Creation
The Technology Evolution Super Team
Jeremy PesnerBrendon Fuhs
Overview
Based on model of technological epochs (Axtell et al., 2013)
– Jeremy presented this. Hopefully you paid attention
Agents now grouped into Zipf-sized firm Agents have access to goods of their coworkers Used to gain a greater understanding of firm size in
the role of technological evolution
Principles of firm size & innovation (Baumol, 2005):
Small, entrepreneurial firms – Breakthrough innovation
Large, corporate firms – Incremental innovation Account for this in the model
Arthur & Beinhocker, Technological Recombination
Technologies have components which are architected towards a specific purpose
Technologies of various fitnesses comprise a fitness landscape, design space
Oftentimes, there is a sudden jump in technological development after a certain point has been reached (Richard Foster)
Creative Destruction – Horse & buggy after car Technological lock-in can prevent adoption of
newer, fitter technologies
Arthur & Beinhocker, Technological Recombination (Part 2)
Many modern technologies are repeatedly used as building blocks for incremental innovation
– Microprocessors
– Chemicals
– Gene manipulation
Breakthrough innovation comes from combining a technology with a physical or social phenomenon
– Physical Phenomena: fire, gravity, air
– Social phenomena: work, friendship, food
– Ex: Google – algorithmic page ranking + human search
Firm Size & Innovation
Different sized firms have different ways of innovating
– Baumol, 2005; Cohen & Klepper, 1996; Stock, Greis & Fischer, 2002
Smaller entrepreneurial firms: Breakthrough innovation
– Crazy ideas
– Nothing to lose, everything to gain
– Doesn't usually work, but when it does...
Larger corporate firms: Incremental innovation
– Want sure bets
– Won't create technologies antithetical to business/organization
– Both needed (Wright Brothers vs Boeing)
Review of Initial Model(just to be nice)
Some number of agents & some number of total goods with randomly distributed fitnesses
Agents can hold and use goods with defined fitnesses. Goods have finite lifetimes
Agents invent a new good by combining 2 existing ones – new fitness value f = [0, g1 + g2]. Some probability that it is marketable
Agents evaluate their own fitness of good = [0, f]. If new good's fitness is greater than least useful good agent owns, will adopt it
Our Model Reimplemented in Python from ground up
– Enables easier visualization
Firms “birth” agents and their goods
– Agents have goods, but considered firm's goods
Agents create their goods from two randomly selected goods within the entire pool of firm goods
– Agents still have their own goods they demand
Firms generate agents with a Zipf probability
– “Zipf Distribution of US Firm Sizes” (Axtell, 2001)
Fitness Boost Large and small firms each need a fitness boost in
order to ascribe to their innovative capabilities
– Formulae included to raise floor for larger firms, ceiling for smaller ones (S = firm size, F = initial fitness):
Playing god, but no other way Large firm's goods will be pretty good, but only so
good Small firm's goods more unpredictable, but could be
game-changing
Experimenting with the model
Case A: No firms
Case B: No firm advantages
Case C: Small firm advantage only
(omega = 0.25)
Case D: Large and Small firm advantages
(omega = 0.25, alpha = 0.5)
Average Market Share vs. Firm Size
Market share of FINAL goods
B: upper right
C: lower left
D: lower right
Average Market Share vs. Firm Size
Market share of ALL goods
B: upper right
C: lower left
D: lower right
Average Market Share vs. Firm Size
Log(avg market share) as a linear function of log(firm size)
Log-slope(exponent in non-logged model)
Log-intercept(multiplier in non-logged model)
R-squared
No advantages, final goods
0.427 0.169 0.0844
No advantages, all goods
0.694 0.0518 0.162
Small firm advantage, final goods
0.1421 0.261 0.00843
Small firm advantage, all goods
0.413 0.0649 0.0531
Both advantages, final goods
0.668 0.125 0.194
Both advantages, all goods
0.879 .03314 0.208
Observations / Interpretation
• Results are a little hairy!
• Smaller slope in experiments indicates small firm advantage
– Particularly notable in experiment B
• However, in experiment D, ω < α
• Small firm success was bounded
– Specific nature of formulae somewhat arbitrary (blame Brendon)
• Future work needed to test different formulae and advantage coefficients