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

AuthorLasse Gerrits,Peter Marks
Published date01 August 2022
Date01 August 2022
DOIhttp://doi.org/10.1177/00953997221098776
Subject MatterArticles
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
1358 Administration & 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.

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