Learn and Adapt, or Perish: The Case of the F35 Lightning II

• Author: Lasse Gerrits,Peter Marks
• Published date: 01 August 2022
• Date: 01 August 2022
• DOI: http://doi.org/10.1177/00953997221098776

https://doi.org/10.1177/00953997221098776

Administration & Society

2022, Vol. 54(7) 1357 –1378

© The Author(s) 2022

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DOI: 10.1177/00953997221098776

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Article

Learn and Adapt,

or Perish: The Case

of the F35 Lightning II

Lasse Gerrits1 and Peter Marks1

Abstract

We assess to what extent a (co)evolutionary macro level approach

enhances our understanding of learning in governance processes. We ask

the question: in what ways do actors learn to improve their chances of

long-term survival in complex governance processes? We deploy a model

of collective decision making moulded upon fitness landscapes to analyze a

longitudinal case study of collective (political and administrative) decision

making, namely the process of developing and acquiring the F35 Lightning II

fighter jet. The study demonstrates that actors learn how to ensure survival

over time but create a failing megaproject in the process.

Keywords

fitness, variation, selection, retention, coevolution, alignment

The Multi-Trillion Fighter Plane

Early 1990s, the US Marine Corps planned for a new fighter jet to replace

the aging Harrier. The US Congress, worried about the prospect of massive

costs associated with the development, procurement, operation, and sup-

port of different aircrafts for the various branches of the US military,

sought to merge the demands of the Marine Corps with those of the US

1Erasmus University Rotterdam, The Netherlands

Corresponding Author:

Lasse Gerrits, Institute for Housing and Urban Development Studies, Erasmus University

Rotterdam, Burgemeester Oudlaan 50, Rotterdam, PA 3062, The Netherlands.

Email: gerrits@ihs.nl

1098776AAS0010.1177/00953997221098776Administration & SocietyGerrits and Marks

research-article2022

1358Administration & Society 54(7)

Airforce and US Navy (Congressional Research Service [CSR], 2020).

The stated goal was to develop three variants of the same basic aircraft

with high commonality to save on building and operations costs (Institute

for Defense Analyses [IDA], 2010). Consequently, the Joint Advance

Strike Technology (JAST) program was established, from which the F35

Lightning II fighter jet emerged.

The program run out of control, subject as it was to massive cost overruns,

severe technical delays, and constant bickering between all actors involved.

The lifetime costs of a single airframe may be up to $1.5 trillion (2015 prices,

see Bender et al., 2015). As such, it has become the single most expensive

project in US-history (Hughes, 2015). It fits in a long history of so-called

megaprojects: governments and private actors engaging in projects that

promise the technological sublime (Frick, 2008) but that in most cases tend

to run out of control (Flyvbjerg, 2014; Flyvbjerg et al., 2003). With so much

evidence about the troublesome nature of megaprojects, one can be forgiven

for asking “haven’t they learnt?” This is especially the case with the F35,

which was preceded by the equally troublesome (but less expensive) F22A

Raptor program of the US Air Force. While the obvious answer to that ques-

tion seems a resounding “no,” a coevolutionary perspective may generate a

different kind of answer that aligns more closely to the nature of such com-

plex processes.

Our contribution to this special issue will assess to what extent a (co)

evolutionary view on learning may enhance our understanding of learning

in governance processes. Generally speaking, evolution and co-evolution

explain the emergence of speciation through variation, selection, and

retention under selection pressure to establish fit with the environment.

Such a fit would ensure long-term survival. We therefore ask the question:

in what ways do actors learn to improve their chances of long-term sur-

vival in complex governance processes? To answer this question, we first

introduce the concept of coevolution and its relationship to learning in

Section 2. From this, we derive a fitness landscape model of collective

decision making that structures the roles, relationships, preferences, and

behaviors of actors, and the extent to which they are successful in achiev-

ing long-term survival, in governance processes (Section 3). In Section 4,

this model is applied to the JAST/JSF-program, followed by the analysis

in what ways the involved actors have learnt to increase their chances of

survival in Section 5. We conclude that actors that have learnt the best

how to survive the changing environment did so at the cost of a dysfunc-

tional technological program that has cost billions of tax money across

the globe. We reflect on different modes of learning in relationship to

coevolution.